Dissemination (including electronic) of all results must acknowledge EU funding. See below for details.

Marco Viceconti and his team at the Insigneo Institute, part of the University of Sheffield have collated and written a report on the CompBioMed solutions and end-user engagement of our Core Partners. We are currently in the process of extending this to our Associate Partners



Title Citation Summary
Thermodynamic and structural insights into the repurposing of drugs that bind to SARS-CoV-2 main protease S. Wan, A. Bhati, A. Wade, D. Alfe, P. V. Coveney, (2021), DOI:10.33774/chemrxiv-2021-03nrl Although researchers have been working tirelessly since the COVID-19 outbreak, there is yet no effective drug found to directly treat the disease. Given the slow pace and substantial costs of new drug discovery and development, repurposing of existing drugs for the ongoing disease becomes an attractive proposition. In the current study, we use molecular dynamics simulation and an ensemble-based free energy approach, namely, enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS), to investigate a subset of the aforementioned compounds.
Anomalous Platelet Transport & Fat-Tailed Distributions C. Kotsalos, K. Z. Boudjeltia, R. Dutta, J. Latt, B. Chopard, (2020), DOI: arXiv:2006.11755 The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation. Here we propose a disruptive view, by showing that the random part of their velocity is governed by a fat-tailed probability distribution, usually referred to as a Lévy flight. Although for small spatio-temporal scales, it is hard to distinguish it from the generally accepted “red blood cell enhanced” Brownian motion, for larger systems this effect is dramatic as the standard approach may underestimate the flux of platelets by several orders of magnitude, compromising in particular the validity of current platelet function tests.
Interpretable pathological test for Cardio-vascular disease: Approximate Bayesian computation with distance learning R. Dutta, K. Zouaoui-Boudjeltia, C. Kotsalos, A. Rousseau, D. R. de Sousa, J.-M. Desmet, A. Van Meerhaeghe, A. Mira, B. Chopard, (2020), arXiv:2010.06465 Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients.
Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow J. W. S. McCullough, P. V. Coveney, (2023), DOI: 10.21203/ Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcation geometry that is representative of the vascular junctions present in arterial and venous domains. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.
High resolution simulation of basilar artery infarct and flow within the circle of Willis J. W. S. McCullough, P. V. Coveney, (2023), DOI: 10.21203/ On a global scale, cerebro-and cardiovascular diseases have long been one of the leading causes of death and disability and their prevalence appears to be increasing in recent times. Understanding potential biomarkers and risk factors will help to identify individuals potentially at risk of suffering an ischemic stroke. However, the widely variable construction of the cerebral vasculature makes it difficult to provide a specific assessment without the knowledge of a patient’s physiology. In this paper we use the 3D blood flow simulator HemeLB to study flow within three common structural variations of the circle of Willis during and in the moments after a blockage of the basilar artery. This tool, based on the lattice Boltzmann method, allows the 3D flow entering the basilar artery to be finely controlled to replicate the cessation of blood feeding this particular vessel-we demonstrate this with several examples including a sudden halt to flow and a gradual loss of flow over three heartbeat cycles. In this work we start with an individualised 3D representation of a full circle of Willis and then construct two further domains by removing the left or right posterior communicating arteries from this geometry. Our results indicate how, and how quickly, the circle of Willis is able to redistribute flow following such a stroke. Due to the choice of infarct, the greatest reduction in flow was observed in the posterior cerebral arteries where flow was reduced by up to 70% in some cases. The high resolution domains used in this study permit the velocity magnitude and wall shear stress to be analysed at key points during and following the stroke. The model we present here indicates how personalised vessels are required to provide the best insight into stroke risk for a given individual.

In Press

Title Citation Summary
Molecular dynamics elucidates the origin of the enhancement of polymer properties by graphene
J. Suter, M. Vassaux, P. V. Coveney, Advanced Materials (2023), DOI: 10.1002/adma.202302237
Using very large-scale classical molecular dynamics we examine the mechanics of nano-reinforcement of graphene-based nanocomposites. Our simulations show that significant quantities of large, defect-free and predominantly flat graphene flakes are required for successful enhancement of materials properties in excellent agreement with experimental and proposed continuum shear-lag theories. The critical length for enhancement is approximately 500nm and 300nm for graphene and GO respectively. The reduction of Young’s modulus in GO results in a much smaller enhancement of the composite’s Young’s modulus. The simulations reveal that the flakes should be aligned and planar for optimal reinforcement. Undulations substantially degrade the enhancement of materials properties.


Title Citation Summary
Long timescale ensemble methods in molecular dynamics: Ligand-protein interactions and allostery in SARS-CoV-2 targets A. Bhati, A. Hoti, A. Potterton, M. K. Bieniek, P. V. Coveney, Journal of Chemical Theory and Computation (2023) DOI: 10.1021/acs.jctc.3c00020 We subject a series of five protein-ligand systems which contain important SARS- CoV-2 targets – 3-chymotrypsin-like protease, papain-like protease and adenosine ribose phosphatase – to long-timescale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10-microsecond simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this timescale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10-microsecond trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long-timescale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that although this is the standard way such quantities are currently reported at long-timescale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages.
Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations K. Ahmad, A. Javed, C. Lanphere, P. V. Coveney, E. V. Orlova, S. Howorka, Nature Communications (2023), DOI: 10.1038/s41467-023-38681-5 DNA can be folded into rationally designed, unique, and functional materials. To fully realise the potential of these DNA materials, a fundamental understanding of their structure and dynamics is necessary, both in simple solvents as well as more complex and diverse anisotropic environments. Here we analyse an archetypal six-duplex DNA nanoarchitecture with single-particle cryo-electron microscopy and molecular dynamics simulations in solvents of tunable ionic strength and within the anisotropic environment of biological membranes. Outside lipid bilayers, the six-duplex bundle lacks the designed symmetrical barrel-type architecture. Rather, duplexes are arranged in non-hexagonal fashion and are disorted to form a wider, less elongated structure. Insertion into lipid membranes, however, restores the anticipated barrel shape due to lateral duplex compression by the bilayer. The salt concentration has a drastic impact on the stability of the inserted barrel-shaped DNA nanopore given the tunable electrostatic repulsion between the negatively charged duplexes. By synergistically combining experiments and simulations, we increase fundamental understanding into the environment-dependent structural dynamics of a widely used nanoarchitecture. This insight will pave the way for future engineering and biosensing applications.
A multiscale computational framework to evaluate flow alterations during mechanical thrombectomy for treatment of ischaemic stroke I. Benemerito, A. Mustafa, N. Wang, A. P. Narata, A. Narracott, Marzo Alberto, Frontiers in Cardiovascular Medicine (2023), DOI:10.3389/fcvm.2023.1117449
The treatment of ischaemic stroke increasingly relies upon endovascular procedures known as mechanical thrombectomy (MT), which consists in capturing and removing the clot with a catheter-guided stent while at the same time applying external aspiration with the aim of reducing haemodynamic loads during retrieval. In this study we present a multiscale computational framework to simulate MT procedures. The developed framework can provide quantitative assessment of clinically relevant quantities such as flow in the retrieval path and can be used to find the optimal procedural parameters that are most likely to result in a favorable clinical outcome.
Statistical Properties of a Virtual Cohort for In Silico Trials Generated with a Statistical Anatomy Atlas A. Mattina, F. Baruffaldi, M. Taylor & M. Viceconti, Ann Biomed Eng (2023), DOI:10.1007/s10439-022-03050-8
Osteoporosis-related hip fragility fractures are a catastrophic event for patient lives but are not frequently observed in prospective studies, and therefore phase III clinical trials using fractures as primary clinical endpoint require thousands of patients enrolled for several years to reach statistical significance. A novel answer to the large number of subjects needed to reach the desired evidence level is offered by In Silico Trials, that is, the simulation of a clinical trial on a large cohort of virtual patients, monitoring the biomarkers of interest. In this work we investigated if statistical aliasing from a custom anatomy atlas could be used to expand the patient cohort while retaining the original biomechanical characteristics.
TIES 2.0: a Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal M. Bieniek, A. Wade, A. Bhati, S. Wan and P. V. Coveney, Journal of Chemical Information and Modeling (2023) DOI:10.1021/acs.jcim.2c01596 Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced Sampling, is a dual-topology approach to RBFE calculations with support for NAMD and OpenMM molecular dynamics engines. The software has been thoroughly validated on publicly available datasets. Here we describe the open source software along with a web portal ( that enables users to perform such calculations correctly and rapidly.


Title Citation Summary
Reducing the Complexity of Musculoskeletal Models Using Gaussian Process Emulators I. Benemerito, E. Montefiori, A. Marzo, C. Mazzà, Applied Sciences DOI:10.3390/app122412932
Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies. In this work we have developed a methodology relying on Sobol’s sensitivity analysis (SSA) for ranking muscles based on their importance to the determination of the joint contact forces (JCFs) in a cohort of older women. Results show that there is a pool of muscles whose personalisation has little effects on the predictions of JCFs, allowing for a reduced but still accurate representation of the musculoskeletal system within shorter timeframes.
Hierarchically Structured Bioinspired Nanocomposites D. Nepal, S Kang, K. M. Adstedt, K. Kanhaiya, M. R. Bockstaller, L. C. Brinson, M. J. Buehler, P. V. Coveney, K. Dayal, J. A. El-Awady, L. C. Henderson, D. L. Kaplan, S. Keten, N. A. Kotov, G. C. Schatz, S. Vignolini, F. Vollrath, Y. Wang, B. I. Yakobson, V. V. Tsukruk, H. Heinz, (2022), DOI:10.1038/s41563-022-01384-1

Next-generation structural materials are expected to be lightweight, high-strength and tough composites with embedded functionalities to sense, adapt, self-repair, morph and restore. This Review highlights recent developments and concepts in bioinspired nanocomposites, emphasizing tailoring of the architecture, interphases and confinement to achieve dynamic and synergetic responses. We highlight cornerstone examples from natural materials with unique mechanical property combinations based on relatively simple building blocks produced in aqueous environments under ambient conditions. A particular focus is on structural hierarchies across multiple length scales to achieve multifunctionality and robustness. We further discuss recent advances, trends and emerging opportunities for combining biological and synthetic components, state-of-the-art characterization and modelling approaches to assess the physical principles underlying nature-inspired design and mechanical responses at multiple length scales. These multidisciplinary approaches promote the synergetic enhancement of individual materials properties and an improved predictive and prescriptive design of the next era of structural materials at multilength scales for a wide range of applications.

FabSim3: An automation toolkit for verified simulations using high performance computing D. Groen, H. Arabnejad, D. Suleimenova, W. Edeling, E. Raffin, Y. Xue, K. Bronik, N. Monnier, P. V. Coveney, Computer Physics Communications DOI:10.1016/j.cpc.2022.108596

Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers.

Parametric Analysis of an Efficient Boundary Condition to Control Outlet Flow Rates in Large Arterial Networks Sharp Chim Yui Lo, J. W. S. McCullough, P. V. Coveney, Sci. Rep., DOI:10.1038/s41598-022-21923-9 Substantial effort is being invested in the creation of a virtual human—a model which will improve our understanding of human physiology and diseases and assist clinicians in the design of personalised medical treatments. A central challenge of achieving blood flow simulations at full-human scale is the development of an efficient and accurate approach to imposing boundary conditions on many outlets. A previous study proposed an efficient method for implementing the two-element Windkessel model to control the flow rate ratios at outlets. Here we clarify the general role of the resistance and capacitance in this approach and conduct a parametric sweep to examine how to choose their values for complex geometries. We show that the error of the flow rate ratios decreases exponentially as the resistance increases. Our findings also establish constraints on the parameters controlling the numerical stability of the simulations. The findings from this work are directly applicable to larger and more complex vascular domains encountered at full-human scale.
Development and performance of a HemeLB GPU code for human-scale blood flow simulation I. Zacharoudiou, J.W.S. McCullough, P.V. Coveney, 282, 108548 (2023), Computer Physics Communications, DOI:10.21203/ In recent years, it has become increasingly common for high performance computers (HPC) to possess some level of heterogeneous architecture – typically in the form of GPU accelerators. In some machines these are isolated within a dedicated partition, whilst in others they are integral to all compute nodes – often with multiple GPUs per node – and provide the majority of a machine’s compute performance. In light of this trend, it is becoming essential that codes deployed on HPC are updated to execute on accelerator hardware. In this paper we introduce a GPU implementation of the 3D blood flow simulation code HemeLB that has been developed using CUDA C++. With HPC positioned on the brink of the exascale era, we use HemeLB as a motivation to provide a discussion on some of the challenges that many users will face when deploying their own applications on upcoming exascale machines
The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase S. Wan, A. P. Bhati, D. W. Wright, A. D. Wade, G. Tresaderm, H. van Vlijmen, P. V. Coveney, Sci. Rep., 12, 10433 (2022), doi: 10.1038/s41598-022-13319-6 Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility A. Wade, A. P. Bhati, S. Wan, P. V. Coveney, J. Chem. Theory Comput., 18, 6, 3972–3987 (2022), doi: 10.1021/acs.jctc.2c00114 The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.
Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors S. Wan, A. Bhati, D. Wright, I. Wall, A. Graves, D. Green, P. V. Coveney, J. Chem. Inf. Model., (2022), DOI: 10.1021/acs.jcim.2c00255 Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.
Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach I. Benemerito, A. P. Narata, A. Narracott, A. Marzo, Annals of Biomedical Engineering, (2022), doi:10.1007/s10439-022-02956-7 Assessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pre- and post stroke velocity and flow wave propagation in a typical arterial network, and Sobol’s sensitivity analysis, supported by the use of Gaussian process emulators, to identify biomarkers linked to cerebral perfusion. We showed that values of pulsatility index of the right anterior cerebral artery > 1.6 are associated with poor perfusion and may require immediate intervention. Three additional biomarkers with similar behaviour, all related to pulsatility indices, were identified. These results suggest that flow pulsatility measured at specific locations could be used to effectively estimate distal cerebral perfusion rates, and ultimately improve clinical diagnosis and management of ischaemic stroke.
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning R. Dutta., K. Z. Boudjeitia., C. Kotsalos., A. Rousseau., D. R. de Sousa., J. Desmet., A. Van Meerhaeghe., A. Mira., B. Chopard, PLOS Comput. Biol., (2022), doi:10.1371/journal.pcbi.1009910 Cardiovascular accidents often result from blood deficiencies, such as platelets dysfunction. Current diagnosis techniques to detect such dysfunctions are not sufficiently accurate and unable to determine which platelet properties are affected. We develop a novel approach to describe in-vitro platelets deposition patterns in terms of clinically meaningful patient specific bio-physical quantities that allow for personalized clinical diagnostics. This approach combines mathematical modeling, statistical inference techniques, machine learning and high performance computation to estimate the values of these clinically relevant platelet properties. We demonstrate our approach on three classes of donors, healthy volunteers, patients subject to dialysis and patients with chronic obstructive pulmonary disease. We claim that our approach opens a paradigm shift for the treatment and diagnosis of cardiovascular diseases, leading to personalized medicine.
Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols A. P. Bhati and P. V. Coveney, J. Chem. Theory Comput., (2022), doi:10.1021/acs.jctc.1c01288 The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.
Hybrid parallelization of molecular dynamics simulations to reduce load imbalance J. Morillo, M. Vassaux, P. V. Coveney, M. Garcia-Gasulla, J. Supercomput. (2022), DOI:10.1007/s11227-021-04214-4 The most widely used technique to allow for parallel simulations in molecular dynamics is spatial domain decomposition, where the physical geometry is divided into boxes, one per processor. This technique can inherently produce computational load imbalance when either the spatial distribution of particles or the computational cost per particle is not uniform. This paper shows the benefits of using a hybrid MPI+OpenMP model to deal with this load imbalance. We consider LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), a prototypical molecular dynamics simulator that provides its own balancing mechanism and an OpenMP implementation for many of its modules, allowing for a hybrid setup. In this work, we extend the current OpenMP implementation of LAMMPS and optimize it and evaluate three different setups: MPI-only, MPI with the LAMMPS balance mechanism, and hybrid setup using our improved OpenMP version.


Title Citation Summary
An efficient, localised approach for the simulation of elastic blood vessels using the lattice Boltzmann method J. W. S. McCullough, P. V. Coveney, Rep. Sci, 11, 24260 (2021), DOI:10.1038/s41598-021-03584-2 Many numerical studies of blood flow impose a rigid wall assumption due to the simplicity of its implementation compared to a full coupling with a solid mechanics model. In this paper, we present a localised method for incorporating the effects of elastic walls into blood flow simulations using the lattice Boltzmann method implemented by the open-source code HemeLB. We demonstrate that our approach is able to more accurately capture the flow behaviour expected in elastic walled vessels than ones with rigid walls. Furthermore, we show that this can be achieved with no loss of computational performance and remains strongly scalable on high performance computers. We finally illustrate that our approach captures the same trends in wall shear stress distribution as those observed in studies using a rigorous coupling between fluid dynamics and solid mechanics models to solve flow in personalised vascular geometries. These results demonstrate that our model can be used to efficiently and effectively represent flows in elastic blood vessels.
High fidelity blood flow in a patient‑specific arteriovenous fistula J. W. S. McCullough and P. Coveney, Sci. Rep. 11:22301 (2021) DOI: 10.1038/s41598-021-01435-8 An arteriovenous fistula, created by artificially connecting segments of a patient’s vasculature, is the preferred way to gain access to the bloodstream for kidney dialysis. The increasing power and availability of supercomputing infrastructure means that it is becoming more realistic to use simulations to help identify the best type and location of a fistula for a specific patient. We describe a 3D fistula model that uses the lattice Boltzmann method to simultaneously resolve blood flow in patient‑specific arteries and veins. The simulations conducted here, comprising vasculatures of the whole forearm, demonstrate qualified validation against clinical data. Ongoing research to further encompass complex biophysics on realistic time scales will permit the use of human‑scale physiological models for basic and clinical medicine.
Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers A. P. Bhati, S. Wan, D. Alfè, A. R. Clyde, M. Bode, L. Tan, M. Titov, A. Merzky, M. Turilli, S. Jha, R. R. Highfield, W. Rocchia, N. Scafuri, S. Succi, D. Kranzlmüller, G. Mathias, D. Wifling, Y. Donon, A. Di Meglio, S. Vallecorsa, H. Ma, A. Trifan, A. Ramanathan, T. Brettin, A. Partin, F. Xia, X. Duan, R. Stevens, P. V. Coveney, Interface Focus, 11:20210018 DOI: 10.1098/rsfs.2021.0018 The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.
Principles of Small-Molecule Transport through Synthetic Nanopores T. Diederichs, K. Ahmed, J. Burns, Q. Nguyen, Z. Siwy, M. Tornow, P. Coveney, R. Tampé, S. Howorka, ACS Nano (2021), DOI: DOI:10.1021/acsnano.1c05139 Synthetic nanopores made from DNA replicate the key biological processes of transporting molecular cargo across lipid bilayers. Understanding transport across the confined lumen of the nanopores is of fundamental interest and of relevance to their rational design for biotechnological applications. Here we reveal the transport principles of organic molecules through DNA nanopores by synergistically combining experiments and computer simulations. Using a highly parallel nanostructured platform, we synchronously measure the kinetic flux across hundreds of individual pores to obtain rate constants. Our findings on these synthetic pores’ structure–function relationship will serve to guide their rational engineering to tailor transport selectivity for cell biological research, sensing, and drug delivery.
Delivering computationally-intensive digital patient applications to the clinic: An exemplar solution to predict femoral bone strength from CT data I. Benemerito, W. Griffiths, J. Allsopp, W. Furnass, P≥ Bhattacharya, X. Li, A. Marzo, S. Wood, M. Viceconti, A. Narracott, CMPB (2021), DOI: DOI:10.1016/j.cmpb.2021.106200 Whilst fragility hip fractures commonly affect elderly people, often causing permanent disability or death, they are rarely addressed in advance through preventive techniques. Quantification of bone strength can help to identify subjects at risk, thus reducing the incidence of fractures in the population. In recent years, researchers have shown that finite element models (FEMs) of the hip joint, derived from computed tomography (CT) images, can predict bone strength more accurately than other techniques currently used in the clinic. The specialised hardware and trained personnel required to perform such analyses, however, limits the widespread adoption of FEMs in clinical contexts. In this manuscript we present CT2S (Computed Tomography To Strength), a system developed in collaboration between The University of Sheffield and Sheffield Teaching Hospitals, designed to streamline access to this complex workflow for clinical end-users. We conclude that the short processing time makes the system compatible with current clinical workflows. The use of open source software and the accurate description of the workflow given here facilitates the deployment in other centres.
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads A. Al Saadi, D. Alfe, Y. Babuji, A. Bhati, B. Blaiszik, A. Brace, T. Brettin, K. Chard, R. Chard, A. Clyde, P. V. Coveney, I. Foster, T. Gibbs, S. Jha, K. Keipert, T. Kurth, D. Kranzlmüller, H. Lee, Z. Li, H. Ma, A. Merzky, G. Mathias, A. Partin, J. Yin, A. Ramanathan, A. Shah, A. Stern, R. Stevens, L. Tan, M. Titov, A. Trifan, A. Tsaris, M. Turilli, H. Van Dam, S. Wan, D. Wifling, 50th International Conference on Parallel Processing (ICPP ’21), August 9-12 (2021), DOI: DOI:10.1145/3472456.3473524 The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe anIntegrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation.
Scalable HPC & AI Infrastructure for COVID-19 Therapeutics H. Lee, A. Merzky, L. Tan, M. Titov, M. Turilli, D. Alfe, A. Bhati, A. Brace, A. Clyde, P. V. Coveney, H. Ma, A. Ramanathan, R. Stevens, A. Trifan, H. Van Dam, S. Wan, S. Wilkinson, S. Jha, Platform for Advanced Scientific Computing Conference (PASC ’21), July 5-9 (2021), DOI: DOI:10.1145/3468267.3470573 COVID-19 has claimed more than 2.7×10^6 lives and resulted in over 124×10^6 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled.
Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation M. Vassaux, S. Wan, W. Edeling and P. V. Coveney, . Chem. Theory. Comput., 17, 5187 (2021), DOI: DOI:10.1021/acs.jctc.1c00526 Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. We find that robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.
The effect of protein mutations on drug binding suggests ensuing personalised drug selection S. Wan, D. Kumar, V. Ilyin, U. Al Homsi, G. Sher, A. Knuth, P. V. Coveney, Sci. Rep., 11, 13452 (2021), DOI: 10.1038/s41598-021-92785-w The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.
In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment F. Margara, Z. J. Wanga, F. Levrero-Florencio, A. Santiago, M. Vázquez, A. Bueno-Orovioa and B. Rodriguez, Progress in Biophysics and Molecular Biology, 159, 58-74 (2021), DOI: 10.1016/j.pbiomolbio.2020.06.007 An investigation of computer models of the heart calibrated against experimental data from human ventricular electrophysiology. The models focus on the effect of electro-mechanical coupling and pharmacological action, opening new avenues for investigations of the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac pathology.
When We Can Trust Computers (and When We Can’t) P. V. Coveney and R. R. HighfieldPhil. Trans. R. Soc. A., 379, 20200409 (2021), DOI: 10.1098/rsta.2020.0067 With the unprecedented rise of computer power, there is a widespread expectation that digital computers could solve any problem. We explore their limits in the domains of science and engineering for simpler as well as more complex systems and discuss where analogue computers stand.
Finite element analysis informed variable selection for femoral fracture risk prediction M. Taylor, M. Viceconti, P. Bhattacharya, X. Li, Journal of the Mechanical Behavior of Biomedical Materials, 118, 104434 (2021) DOI: 10.1016/j.jmbbm.2021.104434 Logistic regression classification (LRC) is widely used to develop models to predict the risk of femoral fracture. LRC models based on areal bone mineral density (aBMD) alone are poor, with area under the receiver operator curve (AUROC) scores reported to be as low as 0.63. This has led to researchers investigating methods to extract further information from the image to increase performance. This raises the question, are we reaching the limits of the information that can be extracted from an image? Finite element analysis was used in combination with active shape and appearance modelling to select variables to develop LRC models of fracture risk. Based on the findings in this paper, it is suggested that we are reaching the limits of the information that can be extracted from an image to predict fracture risk.
Evaluation of patient tissue selection methods for deriving equivalent density calibration for femoral bone quantitative CT analyses C.Winsor, X. Lib, M. Qasim, C. R. Henaka, P. J. Pickhardt, H. Ploeg, M. Viceconti, Bone, 143 (2021) DOI: 10.1016/j.bone.2020.115759 An investigation of computer models of the heart calibrated against experimental data from human ventricular electrophysiology. The models focus on the effect of electro-mechanical coupling and pharmacological action, opening new avenues for investigations of the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac pathology.
TorchMD: A Deep Learning Framework for Molecular Simulations S. Doerr, M. Majewski, A. Pérez, A. Krämer, C. Clementi, F. Noe, T. Giorgino, and G. De Fabritiis, J. Chem. Theory Comput., 17, 4, 2355–2363 (2021), DOI: 10.1021/acs.jctc.0c01343 Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials.
Uncertainty Quantification in Classical Molecular Dynamics S. Wan, R. C. Sinclair and P. V. Coveney, Phil. Trans. R. Soc. A, 379, 20200082 (2021), DOI: 10.1098/rsta.2020.0082 Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results.
The influence of external electric fields on proton transfer tautomerism in the guanine-cytosine base pair A. Gheorghiu, P. V. Coveney and A. A. Arabi, Phys. Chem. Chem. Phys. 23, 6252-6265 (2021), DOI: 10.1039/D0CP06218A The Watson–Crick base pair proton transfer tautomers would be widely considered as a source of spontaneous mutations in DNA replication if not for their short lifetimes and thermodynamic instability. This work investigates the effects external electric fields have on the stability of the guanine–cytosine proton transfer tautomers within a realistic strand of aqueous DNA using a combination of ensemble-based classical molecular dynamics (MD) coupled to quantum mechanics/molecular mechanics (QM/MM).
The Impact of Uncertainty on Predictions of the CovidSim Epidemiological Code W. Edeling, H. Arabnejad, R. Sinclair, D. Suleimenova, K. Gopalakrishnan, B. Bosak, D. Groen, I. Mahmood, D. Crommelin and P. Coveney, Nat Comput Sci, 1, 128–135 (2021), DOI: 10.1038/s43588-021-00028-9 The MRC Centre for Global Infectious Disease Analysis at Imperial College London developed the CovidSim code, which was used to inform the UK Government’s response to the COVID-19 pandemic earlier in the year. We review the publicly available CovidSim epidemiological code by means of a parametric sensitivity analysis and uncertainty quantification and conclude that the model contains a large degree of uncertainty in its predictions, due to its inherent nature.
TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing M. Bieniek, A. Bhati, S. Wan and P. V. Coveney, J. Chem. Theory Comput., 17, 2, 1250–1265 (2021), DOI: 10.1021/acs.jctc.0c01179 Thermodynamic integration with enhanced sampling (TIES) is a formally exact alchemical approach to the calculation of relative binding free energies. We implement a new, flexible-topology superimposition algorithm which improves the precision of the predicted free energies with respect to experimental data.
Pharmaceutical Industry—Academia Cooperation A. Heifetz, P. V. Coveney, D. G. Fedorov, I. Morao, T. James, M. Southey, K. Papadopoulos, M. J. Bodkin, A. Townsend-Nicholson, in: Mochizuki Y., Tanaka S., Fukuzawa K. (eds) Recent Advances of the Fragment Molecular Orbital Method, Springer, Singapore (2021), DOI: 10.1007/978-981-15-9235-5_15 We look at the long history of fruitful cooperation between academia and the pharmaceutical industry, the benefits and challenges for each, and provide some practical solutions, based on our own experiences and specific examples, to make this kind of collaboration successful and rewarding.


Title Citation Summary
Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method A. Heifetz, T. James, M. Southey, M. J. Bodkin, S. Bromidge, Quantum Mechanics in Drug Discovery. Methods in Molecular Biology, vol. 2114, 37-48 (2020) DOI: 10.1007/978-1-0716-0282-9_3 The understanding of binding interactions between a protein and a small molecule plays a key role in the rationalization of potency and selectivity and in design of new ideas. However, even when a target of interest is structurally enabled, visual inspection and force field-based molecular mechanics calculations cannot always explain the full complexity of the molecular interactions that are critical in drug design. In this chapter, we describe the FMO method and illustrate its application in the discovery of the benzothiazole (BZT) series as novel tyrosine kinase ITK inhibitors for treatment of allergic asthma.
Accurate Scoring in Seconds with the Fragment Molecular Orbital and Density-Functional Tight-Binding Methods I. Morao, A. Heifetz, D. G. Fedorov, Quantum Mechanics in Drug Discovery. Methods in Molecular Biology, vol. 2114, 143-148 (2020) DOI: 10.1007/978-1-0716-0282-9_9 The accurate evaluation of receptor-ligand interactions is an essential part of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been combined with the density-functional tight-binding (DFTB) method to compute energy calculations of biological systems in seconds. For the first time, it is now possible to perform FMO calculations in a high-throughput manner.
Femoral neck strain prediction during level walking using a combined musculoskeletal and finite element model approach Z. Altai, E. Montefiori, B. van Veen, M. A. Paggiosi, E. V. McCloskey, M. Viceconti, C. Mazzà, X. Li, PLoS ONE, 16(2):
e0245121 (2020) DOI: 10.1371/journal.pone.0245121
Recently, coupled musculoskeletal-finite element modelling approaches have emerged as a way to investigate femoral neck loading during various daily activities. Combining personalised gait data with finite element models will not only allow us to study changes in motion/movement, but also their effects on critical internal structures, such as the femur. However, previous studies have been hampered by the small sample size and the lack of fully personalised data in order to construct the coupled model. Therefore, the aim of this study was to build a pipeline for a fully personalised multiscale (body-organ level) model to investigate the strain levels at the femoral neck during a normal gait cycle. Five postmenopausal women were included in this study. The current findings suggest that personal variations are substantial, and hence it is important to consider multiple subjects before deriving general conclusions for a target population.
Biorheology of occlusive thrombi formation under high shear: in vitro growth and shrinkage B. J. M. van Rooij, G. Závodszky, A. G. Hoekstra and D. N. Ku, Scientific Reports, 10, 18604 (2020) DOI: 10.1038/s41598-020-74518-7 Occlusive thrombi formed under high flow shear rates develop very rapidly in arteries and may lead to myocardial infarction or stroke. Rapid platelet accumulation (RPA) and occlusion of platelet-rich thrombi and clot shrinkage have been studied after flow arrest. However, the influence of margination and shear rate on occlusive clot formation is not fully understood yet. In this study, the influence of flow on the growth and shrinkage of a clot is investigated.
A Heterogeneous Multi-scale Model for Blood Flow B. Czaja, G. Závodszky, A. Hoekstra, In: Computational Science – ICCS 2020, part of ICCS 2020 Lecture Notes in Computer Science, vol. 12142, 403-409, (2020), DOI: 10.1007/978-3-030-50433-5_31 This research focuses on developing a heterogeneous multi-scale model (HMM) for blood flow. Two separate scales are considered in this study, a Macro-scale, which models whole blood as a continuous fluid and tracks the transport of hematocrit profiles through an advection diffusion solver. And a Micro-scale, which computes directly local diffusion coefficients and viscosities using cell resolved simulations. The coupling between these two scales also includes the use of a surrogate model, which saved local viscosity and diffusion coefficients from previously simulated local hematocrit and shear rate combinations. As the HMM model progresses fewer micro models will be spawned. This is accomplished through the surrogate by interpolating from previously computed viscosities and diffusion coefficients.
Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method A. Heifetz, V. Sladek, A. Townsend-Nicholson, D. G. Fedorov, In: Quantum Mechanics in Drug Discovery, part of Methods in Molecular Biology, vol. 2114, 187-205 (2020), DOI: 10.1007/978-1-0716-0282-9_13 Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes.
Characterizing Rhodopsin-Arrestin Interactions with the Fragment Molecular Orbital (FMO) Method A. Heifetz, A. Townsend-Nicholson, In: Quantum Mechanics in Drug Discovery, part of Methods in Molecular Biology, vol. 2114, 177-186 (2020), DOI: 10.1007/978-1-0716-0282-9_12 Arrestin binding to G protein-coupled receptors (GPCRs) plays a vital role in receptor signaling. Recently, the crystal structure of rhodopsin bound to activated visual arrestin was resolved using XFEL (X-ray free electron laser). However, even with the crystal structure in hand, our ability to understand GPCR-arrestin binding is limited by the availability of accurate tools to explore receptor-arrestin interactions.
Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method A. Heifetz, T. James, M. Southey, I. Morao, D. G. Fedorov, M. J. Bodkin, A. Townsend-Nicholson, In: Quantum Mechanics in Drug Discovery, part of Methods in Molecular Biology, vol. 2114, 163-175 (2020), DOI: 10.1007/978-1-0716-0282-9_11 G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.
Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method A. Heifetz, I. Morao, M. Madan Babu, T. James, M. W. Y. Southey, D. G. Fedorov, M. Aldeghi, M. J. Bodkin, and A. Townsend-Nicholson, J. Chem. Theory Comput. 16, 4, 2814–2824 (2020), DOI: 10.1021/acs.jctc.9b01136 G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information.
Spherization of red blood cells and platelet margination in COPD patients K. Boudjeltia, C. Kotsalos, D. de Sousa, A. Rousseau C. Lelubre, O. Sartenaer, M. Piagnerelli, J. Dohet‐Eraly, F. Dubois, N. Tasiaux, B. Chopard, A. Van Meerhaeghe, Annals Reports 1485, 1, 71-82 (2020), DOI: 10.1111/nyas.14489 Red blood cells (RBCs) in pathological situations undergo biochemical and conformational changes, leading to alterations in rheology involved in cardiovascular events. The shape of RBCs in volunteers and stable and exacerbated chronic obstructive pulmonary disease (COPD) patients was analyzed. The effects of RBC spherization on platelet transport (displacement in the flow field caused by their interaction with RBCs) were studied in vitro and by numerical simulations. RBC spherization was observed in COPD patients compared with volunteers.
Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations P. Herrera-Nieto, A. Pérez, and G. De Fabritiis, J. Chem. Inf. Model. 60, 10, 5003–5010 (2020), DOI: 10.1021/acs.jcim.0c00381 The extreme dynamic behavior of intrinsically disordered proteins hinders the development of drug-like compounds capable of modulating them. There are several examples of small molecules that specifically interact with disordered peptides. However, their mechanisms of action are still not well understood. Here, we use extensive molecular dynamics simulations combined with adaptive sampling algorithms to perform free ligand binding studies in the context of intrinsically disordered proteins. The results show several protein–ligand bound states characterized by the establishment of a loosely oriented interaction mediated by a limited number of contacts between the ligand and critical residues of p27.
Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations P. Herrera-Nieto, A. Pérez & G. De Fabritiis, Scientific Reports 10, 12402 (2020) DOI: 10.1038/s41598-020-69322-2 The exploration of intrinsically disordered proteins in isolation is a crucial step to understand their complex dynamical behavior. In particular, the emergence of partially ordered states has not been explored in depth. The experimental characterization of such partially ordered states remains elusive due to their transient nature. Molecular dynamics mitigates this limitation thanks to its capability to explore biologically relevant timescales while retaining atomistic resolution. Here, millisecond unbiased molecular dynamics simulations were performed in the exemplar N-terminal region of p53. Our research highlights the potential complexity underlying the energy surface of intrinsically disordered proteins.
Hydrodynamic Resistance of Intracranial Flow-Diverter Stents: Measurement Description and Data Evaluation B. Csippa, D. Gyürki, G. Závodszky, I. Szikora and G. Paál, Cardiovascular Engineering and Technology 11, 1–13 (2020) DOI: 10.1007/s13239-019-00445-y Intracranial aneurysms are malformations forming bulges on the walls of brain arteries. A flow diverter device is a fine braided wire structure used for the endovascular treatment of brain aneurysms. This work presents a rig and a protocol for the measurement of the hydrodynamic resistance of flow diverter stents. Hydrodynamic resistance is interpreted here as the pressure loss versus volumetric flow rate function through the mesh structure. The difficulty of the measurement is the very low flow rate range and the extreme sensitivity to contamination and disturbances. Based on our evaluation method a confidence band can be determined for a given deployment scenario. Collectively analysing the hydrodynamic resistance and the geometric indices, a deeper understanding of an implantation can be obtained. Our results suggest that to correctly interpret the hydrodynamic resistance of a scenario, the deployment length has to be considered.
A particle-based model for endothelial cell migration under flow conditions P. S. Zun, A. J. Narracott, P. C. Evans, B. J. M. van Rooij and A. G. Hoekstra, Biomechanics and Modeling in Mechanobiology 19, 681–692 (2020) DOI: 10.1007/s10237-019-01239-w Endothelial cells (ECs) play a major role in the healing process following angioplasty to inhibit excessive neointima. This makes the process of EC healing after injury, in particular EC migration in a stented vessel, important for recovery of normal vessel function. In that context, we present a novel particle-based model of EC migration and validate it against in vitro experimental data. The results of this study support the hypothesis that EC migration is strongly affected by the direction and magnitude of local wall shear stress.
Investigating rolling as mechanism for humeral fractures in non-ambulant infants: a preliminary finite element study Z. Altai, M. Viceconti, X. Li, A. C. Offiah, Clinical Radiology 75, 1, 78 (2020) DOI: 10.1016/j.crad.2019.08.026 This study aims to use personalised computed tomography (CT)-based finite element models to quantitatively investigate the likelihood of self-inflicted humeral fracture in non-ambulant infants secondary to rolling. Results of this study challenge the plausibility of self-inflicted humeral fracture caused by rolling in non-ambulant infants and indicate that it is unlikely for a humeral fracture to result from this mechanism without the assistance of an external force.
AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations A. Pérez, P. Herrera-Nieto, S. Doerr, and G. De Fabritiis, J. Chem. Theory Comput. 16, 7, 4685–4693 (2020) DOI: 10.1021/acs.jctc.0c00205 Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.
Coarse graining molecular dynamics with graph neural networks B. E. Husic, N. E. Charron, D. Lemm, J. Wang, A. Pérez, M. Majewski, A. Krämer, Y. Chen, S. Olsson, G. de Fabritiis, F. Noé and Cecilia Clementi, J. Chem. Phys. 153, 194101 (2020), DOI: 10.1063/5.0026133 Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. We introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.
SkeleDock: A Web Application for Scaffold Docking in PlayMolecule A. Varela-Rial, M. Majewski, A. Cuzzolin, G. Martínez-Rosell and Gianni De Fabritiis, J. Chem. Inf. Model. 60, 6, 2673–2677 (2020), DOI: 10.1021/acs.jcim.0c00143 SkeleDock is a scaffold docking algorithm which uses the structure of a protein–ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that if crystallized fragments of the target ligand are available then SkeleDock can outperform rDock docking software at predicting the binding mode. This Application Note also addresses the capacity of this algorithm to model macrocycles and deal with scaffold hopping.
Large scale relative protein ligand binding affinities using non-equilibrium alchemy V. Gapsys, L. Pérez-Benito, M. Aldeghi, D. Seeliger, Herman van Vlijmen, G. Tresadern, and Bert L. de Groot, Chemical Science, 4, (2020), DOI: 10.1039/C9SC03754C Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. For the first time, a setup is presented for overall high precision and high accuracy relative protein–ligand alchemical free energy calculations based on open-source software.
HPC compact quasi-Newton algorithm for interface problems A. Santiago, M. Zavala-Akéc, R. Borrella, G. Houzeaux, M. Vázquez, Journal of Fluids and Structures, 96, 103009 (2020), DOI: 10.1016/j.jfluidstructs.2020.103009 In this work we present a robust interface coupling algorithm called Compact Interface quasi-Newton (CIQN). It is designed for computationally intensive applications using an MPI multi-code partitioned scheme. The algorithm allows to reuse information from previous time steps, feature that has been previously proposed to accelerate convergence. Through algebraic manipulation, an efficient usage of the computational resources is achieved by: avoiding construction of dense matrices and reduce every multiplication to a matrix–vector product and reusing the computationally expensive loops. This leads to a compact version of the original quasi-Newton algorithm. The novelty of this article lies in the HPC focused implementation of the algorithm, detailing how to fuse and combine the composing blocks to obtain an scalable MPI implementation. Such an implementation is mandatory in large scale cases, for which the contact surface cannot be stored in a single computational node, or the number of contact nodes is not negligible compared with the size of the domain.
Fluid–structure interaction simulations outperform computational fluid dynamics in the description of thoracic aorta haemodynamics and in the differentiation of progressive dilation in Marfan syndrome patients R. Pons, A. Guala, J. F. Rodríguez-Palomares, J. C. Cajas, L. Dux-Santoy, G. Teixidó-Tura, J. J. Molins, M. Vázquez, A. Evangelista and J. Martorell, R. Soc. open sci., 7, 191752 (2020), DOI: 10.1098/rsos.191752 Abnormal fluid dynamics at the ascending aorta may be at the origin of aortic aneurysms. This study was aimed at comparing the performance of computational fluid dynamics (CFD) and fluid–structure interaction (FSI) simulations against four-dimensional (4D) flow magnetic resonance imaging (MRI) data; and to assess the capacity of advanced fluid dynamics markers to stratify aneurysm progression risk. Fluid dynamic simulations of the thoracic aorta require fluid–solid interaction to properly reproduce complex haemodynamics. FSI- but not CFD-derived SSR could help stratifying MFS patients.
Computational biomedicine. Part II: organs and systems – Introduction P. V. Coveney, A. Hoekstra, B. Rodriguez and M. Viceconti, J R Soc Interface Focus, 11, 20200082 (2020), DOI: 10.1098/rsfs.2020.0082 Introduction to the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Haemodynamic flow conditions at the initiation of high-shear platelet aggregation: a combined in vitro and cellular in silico study B. J. M. van Rooij, G. Závodszky, A. G. Hoekstra and D. N. Ku, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Digital blood in massively parallel CPU/GPU systems for the study of platelet transport C. Kotsalos, J. Latt, J. Beny and B. Chopard, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Coupling one-dimensional arterial blood flow to three-dimensional tissue perfusion models for in silico trials of acute ischaemic stroke R. M. Padmos, T. I. Józsa, W. K. El-Bouri, P. R. Konduri, S. J. Payne and A. G. Hoekstra, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
A porous circulation model of the human brain for in silico clinical trials in ischaemic stroke T. I. Józsa, R. M. Padmos, N. Samuels, W. K. El-Bouri, A. G. Hoekstra and S. J. Payne, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Applicability assessment of a stent-retriever thrombectomy finite-element model G. Luraghi, J. Felix Rodriguez Matas, G. Dubini, Francesca Berti, S. Bridio, S. Duffy, A. Dwivedi, R. McCarthy, B. Fereidoonnezhad, P. McGarry, C. B. L. M. Majoie, F. Migliavacca and on behalf of the INSIST investigators, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Electrophysiological and anatomical factors determine arrhythmic risk in acute myocardial ischaemia and its modulation by sodium current availability H. Martinez-Navarro, X. Zhou, A. Bueno-Orovio and B. Rodriguez, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
The EurValve model execution environment M. Bubak, K. Czechowicz, T. Gubała, D. R. Hose, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski and S. Wood, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Towards blood flow in the virtual human: efficient self-coupling of HemeLB J. W. S. McCullough, R. A. Richardson, A. Patronis, R. Halver, R. Marshall, M. Ruefenacht, B. J. N. Wylie, T. Odaker, M. Wiedemann, B. Lloyd, E. Neufeld, G. Sutmann, A. Skjellum, D. Kranzlmüller and P. V. Coveney, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection. We report on the progress of the HemeLB lattice Boltzmann code in simulating 3D macroscopic blood flow on a full human scale. The work is in context of the grand aim to create a virtual human – a personalised, digital copy of an individual that will assist in a patient’s diagnosis, treatment and recovery. Integral to the construction of a virtual human, we outline the implementation of a self-coupling strategy for HemeLB.
Analysis of mechanotransduction dynamics during combined mechanical stimulation and modulation of the extracellular-regulated kinase cascade uncovers hidden information within the signalling noise G. Ascolani, T. M. Skerry, D. Lacroix, E. Dall’Ara and A. Shuaib, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Deep medical image analysis with representation learning and neuromorphic computing N. Getty, T. Brettin, D. Jin, R. Stevens and F. Xia, J R Soc Interface Focus, 11, 20200082 (2020), 10.1098/rsfs.2020.0082 Part of the Computational biomedicine. Part II: organs and systems Theme Issue collection.
Computational biomedicine. Part I: molecular medicine – Introduction S. Wan, A. Potterton, F. S. Husseini, D. W. Wright, A. Heifetz, M. Malawski, A. Townsend-Nicholson and P. V. Coveney, J R Soc Interface Focus, 10, 20190128 (2020), DOI: 10.1098/rsfs.2020.0047 Introduction to the Computational biomedicine. Part I: molecular medicine Theme Issue collection.
Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors S. Wan, A. Potterton, F. S. Husseini, D. W. Wright, A. Heifetz, M. Malawski, A. Townsend-Nicholson and P. V. Coveney, J R Soc Interface Focus, 10, 20190128 (2020), DOI: 10.1098/rsfs.2019.0128 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. This paper presents an application of the ESMACS and TIES methods to compute the binding free energies of a series of ligands at G protein-coupled receptors.
On the faithfulness of molecular mechanics representations of proteins towards quantum-mechanical energy surfaces G. König and S. Riniker, J R Soc Interface Focus, 10, 20190121 (2020), DOI: 10.1098/rsfs.2019.0121 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. In this paper, various classical force fields based on molecular mechanics are assessed against quantum mechanical predictions in order to conclude on their accuracy in modelling the relationship between protein structure and function.
How quickly can we predict trimethoprim resistance using alchemical free energy methods? P. W. Fowler, J R Soc Interface Focus, 10, 20190141 (2020), DOI: 10.1098/rsfs.2019.0141 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. This work discusses the efficiency of the alchemical free energy method in predicting antimicrobial resistance to the antibiotic trimethoprim, as an alternative method to genome sequencing of the pathogen.
Large-scale binding affinity calculations on commodity compute clouds S. J. Zasada, D. W. Wright and P. V. Coveney, J R Soc Interface Focus, 10, 20190133 (2020), DOI: 10.1098/rsfs.2019.0133 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. This paper presents an automated workflow for calculating calculate the binding affinities of compounds bound to proteins known as the binding affinity calculator (BAC). BAC automates the process of calculating free energies from the stage of initial model building, through ensemble averaging, to data analysis.
Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction S. Wan, A. P. Bhati, S. J. Zasada and P. V. Coveney, J R Soc Interface Focus, 10, 20200007 (2020), DOI: 10.1098/rsfs.2020.0007 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. Predicting the binding affinity between molecules accurately and efficiently has posed major theoretical and computational challenges. We review a few methods -including two of our own- in terms of how they respond to those challenges and show how they can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine.
The influence of base pair tautomerism on single point mutations in aqueous DNA A. Gheorghiu, P. V. Coveney and A. A. Arabi, J R Soc Interface Focus, 10, 20190120 (2020), DOI: 10.1098/rsfs.2019.0120 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. Mutations within DNA are crucial to both natural evolution and the occurrence of genetic diseases, and are due to a number of different causes. One such cause is known as tautomerism. This paper investigated its kinetics and thermodynamics.
Quantum computing using continuous-time evolution V. Kendon, J R Soc Interface Focus, 10, 20190143 (2020), DOI: 10.1098/rsfs.2019.0143 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. As digital silicon computers are reaching their limits in terms of speed, other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. This article outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.
Educating and engaging new communities of practice with high performance computing through the integration of teaching and research A. Townsend-Nicholson, J R Soc Interface Focus, 10, 20200003 (2020), DOI: 10.1098/rsfs.2020.0003 Part of the Computational biomedicine. Part I: molecular medicine Theme Issue collection. This article describes how our experience with two university modules taught at University College London (UCL) has informed a strategy that can be applied to modules of universities across Europe and worldwide to increase the representation of women and increase diversity in the field of supercomputing.
Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers F.Levrero-Florencio, F.Margara, E.Zacur, A.Bueno-Orovio, Z.J.Wang, A.Santiago, J.Aguado-Sierra, G.Houzeaux, V.Grau, D.Kay, M.Vázquez, R.Ruiz-Baier, B.Rodriguez,Computer Methods in Applied Mechanics and Engineering, 361 (2020) DOI: 10.1016/j.cma.2019.112762 This study presents in detail the description and implementation of a human-based coupled electromechanical modelling and simulation framework, and a high performance computing study on the sensitivity of mechanical biomarkers to key model parameters. The tools and knowledge generated enable future investigations into disease and drug action on human ventricles.
The influence of red blood cell deformability on hematocrit profiles and platelet margination B. Czaja, M. Gutierrez ,G. Závodszky, D. de Kanter, Α. Hoekstra, O. Eniola-Adefeso, PLoS Comput Biol, 16(3): e1007716, 2020, DOI: 10.1371/journal.pcbi.1007716 The deformability of red blood cells (RBCs) not only allows them to squeeze through small capillaries, yet it also impacts their flow dynamics in the plasma. We simulate varying degrees of deformability to discover that it also affects how platelets flow.
Towards Heterogeneous Multi-scale Computing on Large Scale Parallel Supercomputers S. A. Alowayyed, M. Vassaux, B. Czaja, P. V. Coveney, A. G. Hoekstra, Supercomputing Frontiers and Innovations, 2020, DOI: 10.14529/jsfi1904022 We discuss the heterogeneous multi-scale computing (HMC) pattern as a generalized method of exploiting emerging exascale computing resources, mainly concluding that, considering the subtle interplay between the macroscale model, surrogate models and micro-scale simulations, HMC provides a promising path towards exascale for many multiscale applications.
Lattice-Boltzmann interactive blood flow simulation pipeline S. S. Esfahani, X. Zhai, M. Chen, A. Amira, F. Bensaali, J. AbiNahed, S. Dakua, G. Younes, A. Baobeid, R. A. Richardson and P. V. Coveney, Int J CARS 15, pp. 629–639, 2020, DOI: 10.1007/s11548-020-02120-3 Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering.
Hemelb Acceleration and Visualization for Cerebral Aneurysms S. S. Esfahani, X. Zhai, M. Chen, A. Amira, F. Bensaali, J. AbiNahed, S. Dakua, G. Younes, R. A. Richardson and P. V. Coveney, 2019 IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan, pp. 1376-1380, 2020, DOI: 10.1109/ICIP.2019.8803712 A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm. Optimal treatment requires fast and accurate diagnosis of the aneurysm. HemeLB is a fluid dynamics solver for complex geometries developed to provide neurosurgeons with information related to the flow of blood in and around aneurysms. On a cost efficient platform, HemeLB could be employed in hospitals to provide surgeons with the simulation results in real-time. In this work, we developed an improved version of HemeLB for GPU implementation and result visualization.
Accuracy and Precision of Alchemical Relative Free Energy Predictions With and Without Replica-Exchange S. Wan, G. Tresadem, L. Perez-Benito, H. van Vlijmen, P. V. Coveney, Advanced Theory and Simulations, 3(1), 1900195, 2020, DOI: 10.1002/adts.201900195 Advances in free energy calculations have been fostered by the integration of improved force fields, enhanced sampling methods and increased computer power. We compare the accuracy and precision of relative free energies calculated from standard TIES and two REST‐implemented approaches.


Title Citation Summary
Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach A. Heifetz, T. James, M. Southey, I. Morao, M. Aldeghi, L. Sarrat, D. G. Fedorov, M. J. Bodkin, A. Townsend-Nicholson, Current Opinion in Structural Biology 55, 85-92 (2019) DOI: 10.1016/ There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor–ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanical approaches (QM) are often too computationally expensive, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect.
Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments P. S. Zun, A. J. Narracott, C. Chiastra, J. Gunn and A. G. Hoekstra, Cardiovascular Engineering and Technology 10, 568–582, (2019) DOI: 10.1007/s13239-019-00431-4 Coronary artery restenosis is an important side effect of percutaneous coronary intervention. Computational models can be used to better understand this process. We report on an approach for validation of an in silico 3D model of in-stent restenosis in porcine coronary arteries and illustrate this approach by comparing the modelling results to in vivo data for 14 and 28 days post-stenting. The approach presented here provides a very detailed, location-specific, validation methodology for in silico restenosis models. The model was able to closely match both histology datasets with a single set of parameters. Good agreement was obtained for both the overall amount of neointima produced and the local distribution.
Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer B. J. M. van Rooij, G. Závodszky, V. W. Azizi Tarksalooyeh and A. G. Hoekstra, J. R. Soc. Interface 26, 20190148 (2019) DOI: 10.1098/rsif.2019.0148 Computer simulations were performed to study the transport of red blood cells and platelets in high shear flows, mimicking earlier published in vitro experiments in microfluidic devices with high affinity for platelet aggregate formation. The goal is to understand and predict where thrombus formation starts. We hypothesize that the enlarged cell-depleted layer combined with a sufficiently large platelet flux and sufficiently high shear rates result in an haemodynamic environment that is a preferred location for initial platelet aggregation.
High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence H. Martinez-Navarro, A. Mincholé, A. Bueno-Orovio, B. Rodriguez, Scientific Reports, 9, 16803 (2019), DOI: 10.1038/s41598-019-53221-2 Acute myocardial ischemia is a precursor of sudden arrhythmic death. Variability in its manifestation hampers understanding of arrhythmia mechanisms and challenges risk stratification. Our aim is to unravel the mechanisms underlying how size, transmural extent and location of ischemia determine arrhythmia vulnerability and ECG alterations. High performance computing simulations using a human torso/biventricular biophysically-detailed model were conducted to quantify the impact of varying ischemic region properties, including location (LAD/LCX occlusion), transmural/subendocardial ischemia, size, and normal/slow myocardial propagation. The technology and results presented can inform safety and efficacy evaluation of anti-arrhythmic therapy in acute myocardial ischemia.
Continuum model for flow diverting stents in 3D patient-specific simulation of intracranial aneurysms S. Li, B. Chopard and J. Latt, Journal of Computational Science, 38, 101045 (2019), DOI: 10.1016/j.jocs.2019.101045 The present work extends the framework of screen-based flow diverter model (SFDM) to 3D flows and validates it using actual medical flow diverters in patient specific aneurysms. The numerical tests show that the SFDM can reproduce the results of direct numerical simulation both qualitatively and quantitatively with high precision, and are capable of reducing the simulation time by an order of magnitude or more. The article discusses the procedure required to deploy the model for a given stent and artery.
DeltaDelta neural networks for lead optimization of small molecule potency J. Jiménez-Luna, L. Pérez-Benito, G. Martínez-Rosell, S. Sciabola, R. Torella, G. Tresadern and G. De Fabritiis, Chemical Science, 47, (2019), DOI: 10.1039/C9SC04606B The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks.
Bridging the computational gap between mesoscopic and continuum modeling of red blood cells for fully resolved blood flow C. Kotsalos, J. Latt and B. Chopard J. Comput. Phys, 2019, DOI: 10.1016/ A computational framework for the simulation of blood flow with fully resolved red blood cells using a modular approach that consists of a lattice Boltzmann solver, a novel finite element based solver and an immersed boundary method.
Big data: the end of the scientific method? S. Succi and P. V. Coveney, Philos. T. R. Soc. A., 2019, DOI: 10.1098/rsta.2018.0145 We argue that the boldest claims of big data (BD) are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We propose a synergistic merging, as opposed to antagonism, between BD and mechanistic theory, for a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo.
Red blood cell and platelet diffusivity and margination in the presence of cross-stream gradients in blood flows G. Závodszky, B. van Rooij, B. Czaja, V. Azizi, D. de Kanter and A. G. Hoekstra, Physics of Fluids, 2019, DOI: 10.1063/1.5085881 The radial distribution of cells in blood flow inside vessels is highly non-homogeneous, leading to complex fluid dynamics of which the mechanisms are not fully understood. Single-cell, cell-pair, and large-scale many-cell simulations are performed using a validated numerical model in order to gain insight into this complexity.
Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)—phase II: rupture risk assessment P. Berg, S. Voß, G. Janiga, et al. Int. J. CARS., 2019, 10.1007/s11548-019-01986-2 Assessing the rupture probability of intracranial aneurysms remains challenging. Therefore, hemodynamic simulations are increasingly applied toward supporting physicians during treatment planning. However, due to several assumptions, the clinical acceptance of these methods remains limited.
β-Adrenergic Receptor Stimulation and Alternans in the Border Zone of a Healed Infarct: An ex vivo Study and Computational Investigation of Arrhythmogenesis J. Tomek, G. Hao, M. Tomková, A. Lewis, C. Carr, D. J. Paterson, B. Rodriguez, G. Bub and N. Herring, Front. Physiol., 2019, 10.3389/fphys.2019.00350 Following myocardial infarction (MI), the myocardium is prone to calcium-driven alternans, which typically precedes ventricular tachycardia and fibrillation. We hypothesize that the infarct border zone is most vulnerable to alternans, that β-adrenergic receptor stimulation can suppress this, and investigate the consequences in terms of arrhythmogenic mechanisms.
Predicting Activity Cliffs with Free-Energy Perturbation L. Pérez-Benito, N. Casajuana-Martin, M. Jiménez-Rosés, H. van Vlijmen and G. Tresadern, J. Chem. Theory Comput., 2019, 10.1021/acs.jctc.8b01290 Activity cliffs (ACs) are an important type of structure–activity relationship in medicinal chemistry where small structural changes result in unexpectedly large differences in biological activity. Being able to predict these changes would have a profound impact on lead optimization of drug candidates.
Mechanisms Underlying Allosteric Molecular Switches of Metabotropic Glutamate Receptor 5 C. L. del Torrent, N. Casajuana-Martin, L. Pardo, G. Tresadern and L. Pérez-Benito, J. Chem. Inf. Model., 2019, 10.1021/acs.jcim.8b00924 The metabotropic glutamate 5 (mGlu5) receptor is a class is implicated in several central nervous system disorders, making it a popular drug discovery target, however the origins of its effect are not understood, causing difficulties in a drug discovery context. We juxtapose experimental and simulation results to investigate these effects./td>
Improved biomechanical metrics of cerebral vasospasm identified via sensitivity analysis of a 1D cerebral circulation model A. Melis, F. Moura, I. Larrabide, K. Janot, R. H. Clayton, A. P. Narata and A. Marzo J. Biomech., 201910.1016/j.jbiomech.2019.04.019 Cerebral vasospasm (CVS) is a life-threatening condition that occurs in a large proportion of those affected by subarachnoid haemorrhage and stroke. The aim of this study is to identify alternative biomarkers that could be used to diagnose CVS.
Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors C. L. del Torrent, L. Pérez-Benito and G. Tresadern, Molecules, 201910.3390/molecules24061098 Metabotropic glutamate receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. We review the application of computational methods to the study of this family of receptors.
The effect of boundary and loading conditions on patient classification using finite element predicted risk of fracture Z. Altai, M. Qasim, X. Li, and M. Viceconti, Clin. Biomech., 2019, DOI: 10.1016/j.clinbiomech.2019.06.004 Osteoporotic proximal femoral fractures associated to falls are a major health burden in the ageing society. This study investigates the ability of the Finite Element predicted strength in classifying fracture and non-fractured cases.
Investigating the complex arrhythmic phenotype caused by the gain-of-function mutation KCNQ1-G229D X. Zhou, A. Bueno-Orovio, R. J. Schilling, C. Kirby, C. Denning, D. Rajamohan, K. Burrage, A. Tinker, B. Rodriguez and S. HarmerFront. Physiol., 2019, DOI: 10.3389/fphys.2019.00259 A cardiac electrophysiological disorder that can result in sudden cardiac death is caused by the mutation of protein KCNQ1. We investigate the ionic, cellular and tissue mechanisms underlying the complex phenotype of KCNQ1 mutation using computer modeling and simulations informed by in vitro measurements.
Application of the ESMACS binding free energy protocol to a multi-binding site lactate dehydogenase A ligand dataset D. W. Wright, F. Husseini, S. Wan, C. Meyer, H. van Vlijmen, G. Tresadern, P. V. Coveney, Advanced Theory and Simulations, 2019, DOI: 10.1002/adts.201900194 Fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. We evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS, by comparison to experimental results.
A New Pathology in the Simulation of Chaotic Dynamical Systems on Digital Computers A. Potterton, F. Husseini, M. Southey, M. Bodkin, A. Heifetz, P. V. Coveney, A. Townsend-Nicholson, Advanced Theory and Simulations, 1900125, 2019, DOI:10.1002/adts.201900125 Systematic distortions are uncovered in the statistical properties of chaotic dynamical systems when represented and simulated on digital computers using standard IEEE floating-point numbers. The analysis indicates that the pathology described, which cannot be mitigated by increasing the precision of the floating point numbers, is a presentative example of a deeper problem in the computation of expectation values for chaotic systems.
Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders A. Potterton, F. Husseini, M. Southey, M. Bodkin, A. Heifetz, P. V. Coveney, A. Townsend-Nicholson, Journal of Chemical Theory and Computation, 15 (5), 3316–3330 2019, 10.1021/acs.jctc.8b01270 We present a novel computational method for the reliable prediction of relative drug-target residence time. From ensemble-based steered molecular dynamics simulations, the change in energy between the ligand and water during dissociation is obtained. This energy correlates strongly to the associated experimental residence times of receptor ligands.
Application of ESMACS binding free energy protocols to diverse datasets:Bromodomain-containing protein 4 D. W. Wright, S. Wan, C. Meyer, H. van Vlijmen, G. Tresadern, P. V. Coveney, Scientific Reports, 9, 6017, 2019, 10.26434/chemrxiv.7327019 We investigate the robustness of our ensemble molecular dynamics binding free energy protocols, known as ESMACS, to different choices of forcefield, starting structure and analysis. We examine the influence of multiple trajectories, explicit water molecules and estimates of the entropic contribution to the binding free energy.
PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks J. Jiménez, D. Sabbadin, A. Cuzzolin, G. Martínez-Rosell, J. Gora, J. Manchester, J. Duca, G. De Fabritiis, J. Chem. Inf. Model., 2019, 10.1021/acs.jcim.8b00711 Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. Here, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance.
Shape-Based Generative Modeling for de Novo Drug Design M. Skalic, J. Jiménez, D. Sabbadin, G. De Fabritiis, J. Chem. Inf. Model., 2019, 10.1021/acs.jcim.8b00706 A machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features.
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields J. Wang, S. O, C. Wehmeyer, A. Pérez, N. E. Charron, G. de Fabritiis, F Noé, and C. Clementi ACS Cent. Sci., 2019, 10.1021/acscentsci.8b00913 Coarse-grained models offer the advantage of spanning greater length- and timescales in simulations at the cost of precision. In this paper, we reformulate coarse-graining as a supervised machine learning problem and use statistical learning theory to compare between different models.
Advanced HPC-based Computational Modeling in Biomechanics and Systems Biology M. Vázquez, P. V. Coveney, H. E. Grecco, A. Hoekstra, B. Chopard (editors), Frontiers in Physiology, Frontiers in Applied Mathematics and Statistics and Frontiers in Bioengineering and Biotechnology, e-Book 2019 A collection of articles on how advanced high-performance computing (HPC) methods can facilitate models of biomechanics and systems biology.
Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach. A. Heifetz, T. James, M. Southey, I. Morao, M. Aldeghi, L. Sarrat, D. G. Fedorov, M. J. Bodkin, A. Townsend-Nicholson Curr. Opin. Struct. Biol., 2019, 10.1016/ There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited. Integration of GPCR crystallography or homology modelling with the fragment molecular orbital (FMO) method reveals unprecedented atomistic details.
Computational prediction of GPCR oligomerization. A. Townsend-Nicholson, N. Altwaijry, A. Potterton, I. Morao, A. Heifetz.Curr. Opin. Struct. Biol., 2019, 10.1016/ Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems.
Toward Full GPU Implementation of Fluid-Structure Interaction J. Bény, C. Kotsalos, J. LattIEEE, 2019, 10.1109/ISPDC.2019.000-2 Fluid-structure interaction (FSI) is a notoriously difficult topic in the fields of computational fluid dynamics (CFD) and finite element analysis (FEA), as it requires the deployment of a coupling framework between two different numerical methodologies. In this article, we present a strategy for deploying a FSI system in the many-thread framework of general purpose Graphics Processing Units (gpGPUs).


Title Citation Summary
Understanding Malaria Induced Red Blood Cell Deformation Using Data-Driven Lattice Boltzmann Simulations J. S. Y. Tan, G. Závodszky and P. M. A. Sloot, Computational Science – ICCS 2018, 2018, DOI: 10.1007/978-3-319-93698-7_30 Malaria remains a deadly disease that affected millions of people in 2016. Among the five Plasmodium (P.) parasites which contribute to malaria in humans, P. falciparum is lethal and responsible for the majority of cases. In this study we use lattice Boltzmann simulations to investigate infected red blood cells.
Strategies of data layout and cache writing for input-output optimization in high performance scientific computing: Applications to the forward electrocardiographic problem L. Cardone-Noott, B. Rodriguez, A. Bueno-Orovio PloS One, 2018, DOI: 10.1371/journal.pone.0202410 Input-output (I/O) optimization at the low-level design of data layout on disk drastically impacts the efficiency of high performance computing (HPC) applications. We present a novel low-level data layout for HPC applications, fully independent of the number of dimensions in the dataset.
Are CT-Based Finite Element Model Predictions of Femoral Bone Strengthening Clinically Useful? M. Viceconti, M. Qasim, P. Bhattacharya and X.Li, Curr. Osteoporosis Rep., 2018, DOI: 10.1007/s11914-018-0438-8 This study reviews the available literature to compare the accuracy of various imaging techniques and combinations of such with computer models in predicting bone strength.
Ensemble-based replica exchange alchemical free energy methods: the effect of protein mutations on inhibitor binding A. P. Bhati, S. Wan, and P. V. Coveney, J. Chem. Theory Comput., 2018, 10.1021/acs.jctc.8b01118 The accurate prediction of the binding affinity changes of drugs caused by protein mutations is a major goal in clinical personalized medicine. We have developed TIES, an ensemble-based free energy approach which yields accurate, precise, and reproducible binding affinities.
Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations J. Dakka, K. Farkas-Pall, M. Turilli, D. W. Wright, P. V. Coveney, S. Jha, ArXiv, 2018, The efficacy of drug treatments depends on how tightly the drug’s small molecules bind to their target proteins. We introduce the high-throughput binding affinity calculator (HTBAC), a molecular dynamics framework, as a step towards rapid and accurate quantification of drug-protein interactions and towards surmounting the grand challenge of computational chemistry which could revolutionize drug design and provide the platform for patient-specific medicine.
Identifying inter-helical interactions involved in GPCR structure-function and the forces that determine ligand residence time. A. Heifetz, A. Potterton, I. Morao, T. James, M. Southey, D. Fedorov, M.Bodkin, A. Townsend-NicholsonAbstracts of Papers of the American Chemical Society, 2018, 10.1016/ There has been a recent and prolific expansion in the number of GPCR crystal structures being solved: in both active and inactive forms and in complex with ligand, with G protein and with each other. Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems.
Fully coupled Fluid-electro-mechanical model of the human heart for supercomputers Santiago A, Zavala‐Aké M, Aguado‐Sierra J, Doste R, Gómez S, Arís R, Cajas J C, Casoni E, Vázquez M, Int. J. Numer. Meth. Biomed. Engng., 2018, 10.1002/cnm.3140 The first fluid-electro-mechanical model of the human heart is presented. Such a model allows to analyse all the physics involved in the heartbeat in a whole-heart geometrical description. Such an integrative description of the heartbeat provides to a better understanding of complex cardiopathies.
Implications of bipolar voltage mapping and magnetic resonance imaging resolution in biventricular scar characterisation after myocardial infarction M. López-Yunta, D. G. León, J. M. Alfonso-Almazán, M. Marina-Breysse, J. G. Quintanilla, J. Sánchez-González, C. Galán-Arriola, V. Cañadas-Godoy, D. Enríquez-Vázquez, C. Torres, B. Ibáñez, J. Pérez-Villacastín, N. Pérez-Castellano, J. Julie, M. Vázquez, J. Aguado-Sierra, D. Filgueiras-Rama, Europace, 2018, 10.1093/europace/euy192 Scar characterization using different cardiac imaging modalities is a common clinical approach to stratify the risk of ventricular arrhythmia and identify potential target areas for catheter-based ablation after myocardial infraction. On a sample of pigs we perform a study of the differences in scar characterization using bipolar voltage mapping compared with state-of-the-art in vivo delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging and ex vivo T1 mapping.
Influence of fiber connectivity in simulations of cardiac biomechanics D. Gil, R. Aris, A. Borras, E. Ramirez, R. Sebastian, M. Vazquez, International Journal of Computer Assisted Radiology and Surgery, 2018, 10.1007/s11548-018-1849-9 We explore the influence of the fiber distribution that is used in cardiac simulations, and show that fibers extracted from experimental data produce functional scores closer to healthy ranges than mathematical models. We conclude that deep knowledge of the cardiac fiber field is important to achieve more realistic results in computational modelling.
PlayMolecule BindScope: large scale CNN-based virtual screening on the web M. Skalic, G. Martínez-Rosell, J. Jiménez, G. De Fabritiis, Bioinformatics, 2018, 10.1093/bioinformatics/bty758 A deep learning method to distinguish active from non-active ligands for large-scale virtual screening of drug candidates, which is made available in an easy-to-use web platform. Bindscope accelerates the initial stages of drug-discovery, speeding up the time required for novel drug development.
Numerical Investigation of the Effects of Red Blood Cell Cytoplasmic Viscosity Contrasts on Single Cell and Bulk Transport Behaviour M. de Haan, G. Závodszky, V. Azizi, A. G. Hoekstra, Applied Sciences, 8(9), 1616, 2018, 10.3390/app8091616 Assessing the influence of internal viscosity of red blood cells on the properties of whole blood.
Cell-resolved blood flow simulations of saccular aneurysms: effect of pulsatility and aspect ratio B. Czaja, G. Závodszky, V. Azizi Tarksalooyey, A. G. Hoekstra, Journal of the Royal Society Interface, 2018, 10.1098/rsif.2018.0485 How geometrical properties of aneurysms influence the transport of platelets into these aneurysms.
Inflow and outflow boundary conditions for 2D suspension simulations with the immersed boundary lattice Boltzaman method V. Azizi Tarksalooyeh, G. Závodszky, B. J. M. van Rooij, A. G. Hoekstra, Computers & Fluids, 2018, 10.1016/j.compfluid.2018.04.025 An algorithm for establishing inlet and outlet conditions for simulations of blood flow, explicitly following every single red blood cell in the blood.
Patterns for High Performance Multiscale Computing S. Alowayyed, T. Piontek, J. L. Suter, O. Hoenen, D. Groen, O. Luk, B. Bosak, P. Kopta, K. Kurowski, O. Perks, K. Brabazon, V. Jancauskas, D. Coster, P.V. Coveney, A.G. Hoekstra, Future Generation Computer Systems, 91, 335-346 2018, 10.1016/j.future.2018.08.045 A generic implementation to effectively use supercomputers for multiscale models, including biomedical ones, focusing on automated execution on High-Performance Computers (HPC), delivering performance benefits from both end-user and HPC-system-level perspectives.
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis A. Nikishova, L. Veen, P. Zun, A. G. Hoekstra, Cardiovascular Engineering and Technology, 1-4, 2018, 10.1007/s13239-018-00372-4 Estimating the uncertainties in models of scar tissue prediction in treatment of stenosed coronary arteries.
Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation with High Performance Computing R. Dutta, B. Chopard, J. Latt, F. Dubois, K.Z. Boudjeltia, A. Mira, Front Physiol, 2018, 10.3389/fphys.2018.01128 Existing clinical tests to detect cardio/cerebrovascular diseases (CVD) are ineffectual, in part because they do not consider different stages of platelet activation or the dynamics involved in platelet interactions. Here, we devise a Bayesian inferential scheme for the estimation of these dynamics and support that our approach can be used to build a new generation of personalized platelet functionality tests for CVD detection.
LigVoxel: Inpainting binding pockets using 3D-convolutional neural networks M. Skalic, A. Varela-Rial, J. Jimenez, G. Martinez-Rosell, G. De Fabritiis, Bioinformatics , 2018, 10.1093/bioinformatics/bty583 A proposition of a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. This computational method speeds up the design of novel molecules, and assists the improvement of potential drug candidates.
Simulations meet machine learning in structural biology A. Perez, G. Martinez-Rosell, G. De Fabritiis, Current Opinion in Structural Biology , 2018, 10.1016/ Review article on the impact of the latest machine learning methods in molecular dynamics simulations and structural biology and a future perspective on the field.
Left Ventricular Trabeculations Decrease the Wall Shear Stress and Increase the Intra-Ventricular Pressure Drop in CFD Simulations F. Sacco, B. Pain, O. Lehmkuhl, T.L. Iles, P.A. Iaizzo, G. Houzeaux, M. Vazquez, C. Butakoff, J. Aguado-Sierra, Frontiers in Physiology, 2018, 10.3389/fphys.2018.00458 An examination of the impact of the trabeculae and papillary muscles of the heart on blood flow using high performance computing (HPC) and models reconstructed from high-resolution magnetic resonance images of ex-vivo human hearts.
Evaluating the roles of detailed endocardial structures on right ventricular haemodynamics by means of CFD simulations F. Sacco, B. Pain, O. Lehmkuhl, T.L. Iles, P.A. Iaizzo, G. Houzeaux, M. Vazquez, C. Butakoff, J. Aguado-Sierra, International Journal for Numerical Methods in Biomedical Engineering, 2018, 10.1002/cnm.3115 A detailed computational fluid dynamics study is performed in four human male and female heart geometries, with wall shear stress, pressure drop and turbulence as calculated quantities. We find that neglecting internal structures in cardiac models may lead to inaccurate conclusions about the aforementioned quantities.
A mechanistic model for predicting cell surface presentation of competing peptides by MHC class I molecules D. S. M. Boulanger, R. C. Eccleston, A. Phillips, P. V. Coveney, T. Elliott, N. Dalchau, Frontiers Immunology, 2018, 10.3389/fimmu.2018.01538 We develop and experimentally verify a mechanistic model for presentation of peptides to major histocompatibility complex-I (MHC-I) molecules. The resulting model can be used to predict key steps in the processing of intracellular peptides, which play an important role in inflammatory immune responses to viruses and cancer.
Validation of patient-specific cerebral blood flow simulation using transcranial Doppler measurements D. Groen, R. A. Richardson, R. Coy, U. D. Schiller, H. Chandrashekar, F. Robertson, P. V. Coveney, Frontiers Physiology, 2018, 10.3389/fphys.2018.00721 We show that the HemeLB simulation code is able to reproduce velocities measured using transcranial Doppler in a cerebral artery. HemeLB allows the study of blood flow systems such as aneurysms, and by improving our understanding through simulation we can improve and discover new treatments and preventions.
PolNet: A Tool to Quantify Network-Level Cell Polarity and Blood Flow in Vascular Remodeling M. O. Bernabeu, M. L. Jones, R. W. Nash, A. Pezzarossa, P. V. Coveney, H. Gerhardt, and C. A. Franco, Biophysical Journal, 114 (9), 2052-2058 2018, 10.1016/j.bpj.2018.03.032 PolNet is an open-source software tool for the study of blood flow and cell-level biological activity during vessel morphogenesis. PolNet will be a powerful analysis method to address the complexity of endothelial cell biology at the network level in intact organs.
Uncertainty Quantification in Alchemical Free Energy Methods A. Bhati, S. Wan, Y. Hu, B. Sherborne, P. V. Coveney, Journal of Chemical Theory and Computation, 14 (6), 2867-2880 2018, 10.1021/acs.jctc.7b01143 This study provides a systematic approach to uncertainty quantification based on ensemble simulations, which is generally applicable to all free energy calculation methods that draw on classical molecular dynamics.
Load balancing of parallel cell-based blood flow simulations S. Alowayyed, G. Závodszky, V. Azizi and A. G. Hoekstra J. Comput. Sci., 2018, 10.1016/j.jocs.2017.11.008 The non-homogeneous distribution of computational costs is often challenging to handle in highly parallel applications. Using a methodology based on fractional overheads, we formulate and validate a model for the fractional load imbalance and compare it with other sources of overhead, in particular the communication overhead.
Investigating the mechanical response of paediatric bone under bending and torsion using finite element analysis Z. Altai, M. Viceconti, A. C. Offiah, X. Li, Biomechanics and Modeling in Mechanobiology, 2018, 10.1007/s10237-018-1008-9 This is the first study to quantitatively analyse the infant bone strength under different loading conditions. The results are precious to the research into childhood bone diseases and fractures, especially those in the very young age range. This research could contribute to new medical treatments and preventions of bone diseases and fractures.
Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature A. Patronis, R. A. Richardson, S. Schmieschek, B. J. N. Wylie, R. W. Nash, P. V. Coveney, Frontiers in Physiology, 2018, 10.3389/fphys.2018.00331 We have applied our magnetic-drug targeting model to paramagnetic-nanoparticle-laden flows in a geometry obtained from an MRI scan and we note a strong dependence of the particle density on the strength of the magnetic forcing and the velocity of the background fluid flow.
Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation L. Pérez-Benito, H. Keränen, H. van Vlijmen & G. Tresadern, Scientific Reports, 8, 4833, 2018, 10.1038/s41598-018-23039-5 his work shows that computational methods can help to predict the interaction energy between candidate drug molecules and their target protein, although the inherent flexibility of proteins makes this more difficult than expected. Large computational resources are required for these accurate calculations, but they could greatly enhance the role of computational assisted drug design.
KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks J. Jimenez, M. Skalic, G. Martinez-Rosell, G. De Fabritiis, J. Chem. Inf. Model.,, 52 (8), 287-296, 2018, 10.1021/acs.jcim.7b00650 A deep learning method to predict how strongly ligands bind to proteins, providing fast predictions with similar accuracy compared with other state-of-the-art methods.
Computational Methods for GPCR Drug Discovery A. Heifetz, Springer, 1705, 2018, 10.1007/978-1-4939-7465-8 This book provides a unique overview of modern computational strategies and techniques employed in the field of G protein-coupled receptors (GPCRs) drug discovery, including structure- and ligand-based approaches and cheminformatics.
Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding A. Potterton, A. Heifetz, A. Townsend-Nicholson, Methods Mol Biol., 1705, 335-343, 2018, 10.1007/978-1-4939-7465-8_15 We describe a protocol by which historic ligand binding data and computational models that the former inspire may be used together to understand ligand binding.
Unlocking data sets by calibrating populations of model to data density: A study in atrial electrophysiology B. A. J. Lawson, C. C. Drovandi, N. Cusimano, P. Burrage, B. Rodriguez, K. Burrage, Science Advances, 4 (1), 2018, 10.1126/sciadv.1701676 This study describes the ability of a parameter sampling algorithm to produce populations of models calibrated to data distributions.
Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery A. Heifetz, M. Southey, I. Morao, A. Townsend-Nicholson, M. Bodkin, Methods Mol Biol., 1705, 375-394 2018, 10.1007/978-1-4939-7465-8_19 This book chapter explores drug interaction with proteins and the modern computational strategy of Hit-to-Lead and Lead Optimization Stages of Structure-Based G protein-coupled receptors (GPCR) Drug Discovery. By exploring and developing such computational methods, we move closer to a future where drug design is regularly informed by simulations.
The application of the screen model for stents in cerebral aneurysms S. Li, J. Latt, B. Chopard, Computer Fluids., 172, 651-660 2018, 10.1016/j.compfluid.2018.02.007 Cerebral aneurysms can be treated by inserting a flow-diverter in the parent artery. This study advances a previous model incorporating a flow-diverter by validating a proposed model for complex flow problems and quantifying the benefit of the approach in term of computing speed and accuracy.


Title Citation Summary
Dynamic and Kinetic Elements of µ-Opioid Receptor Functional Selectivity A. Kapoor, G. Martinez-Rosell, D. Provasi, G. Fabritiis, M Filizola, Scientific Reports, 7, 11255 2017, 10.1038/s41598-017-11483-8 An application of high-throughput methods and kinetic analysis to an important protein family for drug-targeting (GPCR), related to mental-health diseases and cancer. These results provide important insights to develop better drugs for GPCR related diseases.
Modelling variability in cardiac electrophysiology: a moment-matching approach E. Tixier, D. Lombardi, B. Rodriguez, J-F. Gerbeau J. R. Soc. Interface., 2017, 10.1098/rsif.2017.0238 We present a method which serves the general purpose of estimating cardiac model parameters from a set of measurements of electrophysiology. The proposed approach may be a new way to investigate features observed in electrophysiology that are experimentally difficult to assess and may have potentially important implications in drug safety pharmacology.
Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity E. Passini, O. J. Britton, H. R. Lu, J. Rohrbacher, A. N. Hermans, D. J. Gallacher, R. J. H. Greig, A. Bueno-Orovio, B. Rodriguez Front. Physiol., 2017, 10.3389/fphys.2017.00668 Early prediction of damage to the heart muscle is critical for drug development. Our study demonstrates that human in silico drug trials constitute a powerful methodology for the prediction of early muscle damage, with better inference potential than equivalent animal models.
Model for pressure drop and flow deflection in the numerical simulation of stents in aneurysms. International journal for numerical methods in biomedical engineering S. Li, J. Latt, B. Chopard, International Journal for Numerical Methods in Biomedical Engineering, 2017, 10.1002/cnm.2949 Cerebral aneurysms can be treated by inserting a flow-diverter in the parent artery. This study proposes simulations which incorporate a description of a flow-diverter, including patient specific cases. Through simulations we advance our understanding of this aneurysm treatment, leading to improvements and new approaches.
δ‐cells and β‐cells are electrically coupled and regulate α‐cell activity via somatostatin L. J. B. Briant, T. M. Reinbothe., I. Spiliotis, C. Miranda, B. Rodriguez and P. Rorsman J. Physiol., 2017, 10.1113/JP274581 Glucagon, the body’s principal hyperglycaemic hormone, is released from α‐cells of the pancreatic islet. Secretion of this hormone is dysregulated in type 2 diabetes mellitus but the mechanisms controlling secretion are not well understood. In this study, we explore the importance of one such candidate mechanism by using an optogenetic strategy.
Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization A. McCarhty, B. Rodriguez, A. Minchole ArXiv, 2017, arXiv:1712.03353 The electrical signals that cause the heart to contract propagate through the torso and we can be recorded on an electrocardiogram (ECG). Cardiac simulators replicate this propagation. We develop a method to infer parameters that improve the fit of a state-of-the-art cardiac simulator.
High-throughput Binding Affinity Calculations at Extreme Scales J. Dakka, K. Farkas-Pall, D. W. Wright, S. J. Zasada, V. Balasubramanian, S. Wan, P. V. Coveney, S. Jha, ArXiv, 2017, Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to molecular interactions between target proteins and the drug. Using multi-stage pipelines of molecular simulations, we can gain insights into the binding free energy between the two and the residence time for a drug, which can inform both stratified and personal treatment regimes and drug development.
Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing R. Dutta, B. Chopard, J. Lätt, F. Dubois, K. Boudjeltia, A. Mira, arXiv, 2017, We propose a methodology which combines clinical images of platelet deposition in the blood, a mathematical model of the deposition process, and a HPC machine-learning approach which calibrates the model parameters to match the clinical images. The combined approach allows us to determine the deposition rates of platelets and detect anomalies, and, in turn, to contribute to a new generation of personalized platelet functionality tests.
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances A. Lyon, A. Michole, J.P.Martinez, P.Laguna, B. Rodriguez, Journal of the Royal Society Interface, 15 (138)2017, 10.1098/rsif.2017.0821 This is a review of the computational techniques that have been proposed for ECG analysis and computer simulations.
In silico evaluation of arrhythmia X. Zhou, A. Bueno-Orovio, B-Rodriguez, Current Opinion in Physiology, 1, 95-103, 2017, 10.1016/j.cophys.2017.11.003 This study highlights key studies in the field of computational simulations of arrhythmia
Host genotype and time dependent antigen presentation of viral peptides: predictions from theory R. Eccleston, P. V. Coveney, and N. Dalchau, Scientific Reports, 7 (1), 14367, 2017, 10.1038/s41598-017-14415-8 We construct a model of the recognition of HIV infection by the MHC (Major Histocompatibility Complex) class I pathway. The model is predictive and useful for helping to design experiments that provide a mechanistic understanding of immune recognition.
Phenotypic variability in LQT3 human induced pluripotent stem cell-derived cardiomyocytes and their response to antiarrhythmic pharmacologic therapy: An in silico approach M. Paci, E. Passini, S. Severi, J. Hyttinen, B. Rodriguez, Heart Rhythm, 2017, 10.1016/j.hrthm.2017.07.026 The study demonstrates, through the simulation of cell populations, the effect mutations in the electrophysiology of stem cell derived cardiomyocytes.
β-adrenergic receptor stimulation inhibits proarrhythmic alternans in post-infarction border zone cardiomyocytes: a computational analysis MJ. Tomek, B. Rodriguez, G. Bub, J. Heijman, American Journal of Physiology – Heart and Circulatory Physiology, 2017, 10.1152/ajpheart.00094.2017 The study demonstrates the anti-arrhythmic effects of beta-adrenergic stimulation post-myocardial infarction using detailed computer simulations.
Cellular Level In-silico Modeling of Blood Rheology with An Improved Material Model for Red Blood Cells G. Závodszky, B. van Rooij, V. Azizi and A. Hoekstra, Front. Physiol., 2017, 10.3389/fphys.2017.00563 A detailed model of blood flow based on modelling every single cell in a cubic millimeter of blood and validation against experimental data.
Multiscale Computing in the Exascale Era S. Alowayyed, D. Groen, P. V. Coveney, A. G. Hoekstra, Journal of Computational Science, 2017, 10.1016/j.jocs.2017.07.004 A vision on how to execute simulations that span multiple scale on the most powerful supercomputers that exist today, and those that will be available in the near future.
Rapid and accurate assessment of GPCR–ligand interactions Using the fragment molecular orbital-based density-functional tight-binding method I. Morao, D. G. Fedorov, R. Robinson, M. Southey, A. Townsend-Nicholson, M. J. Bodkin, A. Heifetz, Journal of Computational Science, 2017, 10.1002/jcc.24850 This paper explores drug interaction with proteins, G protein-coupled receptors, that are a common target for pharmaceuticals. With the particular computer simulation method harnessed in this work, the computational cost is decreased 1000-fold. By exploring and developing such computational methods, we move closer to a future where drug design is regularly informed by simulations.
The role of multiscale protein dynamics in antigen presentation and T lymphocyte recognition R. C. Eccleston, S. Wan, N. Dalchau, P. V. Coveney, Frontiers in Immunology, 2017, 10.3389/fimmu.2017.00797 We advocate a mechanistic description of antigen presentation and TCR (T-cell receptor), which involves multiscale modelling approaches collectively span several length and time scales. The approaches are capable of furnishing reliable biological descriptions that are difficult for experimentalists to provide.
A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data P. S. Zun, T. Anikina, A. Svitenkov, A. G. Hoekstra, Frontiers in Physiology, 8, 1-12, 2017, 10.3389/fphys.2017.00284 A model for scar tissue in coronary arteries after treatment of stenosis compared to data from animal experiments.
Exact solutions to the fractional time-space Bloch–Torrey equation for magnetic resonance imaging A. Bueno-Orovio, K. Burrage, Commun Nonlinear Sci Numer Simulat., 52, 91-109, 2017, 10.1016/j.cnsns.2017.04.013 This study demonstrate the exact solutions to the fractional time-space Bloch-Torrey equation for application in magnetic resonance imaging.
An Ensemble-Based Protocol for the Computational Prediction of Helix-Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics N. Altwaijry, M. Baron, D. Wright, P. V. Coveney, A. Townsend-Nicholson, Journal of Chemical Theory & Computation, 13 (5), 2254-2270, 2017, 10.1021/acs.jctc.6b01246 We provide a systematic, reproducible, and reliable protocol for determining the specific points of interaction between GPCR dimers. Our method is of great utility in further understanding GPCR function and also has broad applicability to many different types of membrane proteins.
Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation S. Wan, A. Bhati, S. Skerratt, K. Omoto, V. Shanmugasundaram, S. Bagal, P. V. Coveney, Journal of Chemical Information and Modelling, 57 (4), 897-909, 2017, 10.1021/acs.jcim.6b00780 The paper presents free energy methods which could be used as tools to guide lead optimization efforts across multiple prospective structurally enabled programs in the drug discovery setting for a wide range of compounds and targets.
Opinion: Is big data just big hype? P. V. Coveney and R. Highfield, Longevity Bulletin: Big data in health, Institute and Faculty of Actuaries, 11-12, 2017, ISSN 2397-7213 To effectively use the explosion in big data, we need to understand the characteristics and sensitivity of the complex systems. The paper emphasises the importance of tools and models for pattern extraction and visualization, which need to be truly predictive.
Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: a Computational Study S. Wan, A. P. Bhati, S. J. Zasada, I. Wall, D. Green, P. Bamborough, and P. V. Coveney, J. Chem. Theory Comput., 13 (2), 784-795, 2017, 10.1021/acs.jctc.6b00794 In collaboration with GlaxoSmithKline, we apply our ensemble based free energy approaches to accurately rank ligands by their binding free energies. The approach offers a long awaited development in the field of structure-based drug design.
Functional identification of islet cell types by electrophysiological fingerprinting L. J. B. Briant, Q. Zhang, E. Vergari, J. A. Kellard, B. Rodriguez, F. M. Ashcroft, P. Rorsman, Journal of Royal Society Interface, 14 (128), 1-20, 2017, DOI: 10.1098/rsif.2016.0999 This study shows the identification of islet cell types using electrophysiological information.
Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics C. Shanchez, A. Bueno-Orovio, E. Pueyo, B. Rodriguez, Front. Bioeng. Biotechnol., 5, 1-13, 2017, DOI: 10.3389/fbioe.2017.00029 The study demonstrates HPC simulations of differences in atrial arrhythmias using six heterogeneous human three-dimensional models of atrial electrophysiology
Rapid, accurate, precise and reliable relative free energy prediction using ensemble based thermodynamic integration A. Bhati, S. Wan, D. Wright, P. V. Coveney, Journal of Chemical Theory and Computation, 13 (1), 210-222, 2017, 10.1021/acs.jctc.6b00979 We apply a systematic protocol for uncertainty quantification to a number of popular free energy methods. With a reliable measure of error estimation, ensemble-based simulations can be used to predict relative free energies accurately.


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