Publications

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

Preprints

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, (2021), arXiv:2108.08783
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
Anomalous Platelet Transport & Fat-Tailed Distributions C. Kotsalos, K. Z. Boudjeltia, R. Dutta, J. Latt, B. Chopard, (2020), DOI: arXiv:2006.11755
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

In Press

Title Citation Summary
High fidelity physiological blood flow in patient-specific arteriovenous fistula for clinical applications J. W. S. McCullough and P. Coveney, Sci. Rep. in press (2021) arXiv:2012.04639 We use the lattice Boltzmann method to simulate blood flow through an arteriovenous fistula as part of a vasculature structure extending across the whole forearm. Peroperative clinical data show very good agreement, making this work a step towards developing virtual-human–scale physiological models.

2021

Title Citation Summary
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. The single-channel transport kinetics are close to the theoretical maximum, while selectivity is determined by the interplay of cargo charge and size, the pores’ sterics and electrostatics, and the composition of the surrounding lipid bilayer. The narrow distribution of transport rates implies a high structural homogeneity of DNA nanopores. The molecular passageway through the nanopore is elucidated via coarse-grained constant-velocity steered molecular dynamics simulations. The ensemble simulations pinpoint with high resolution and statistical validity the selectivity filter within the channel lumen and determine the energetic factors governing transport. 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.
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. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver100×to 1000×improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼10^11 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the USDOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.
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. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, 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.
Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit D. Suleimenova, H. Arabnejad, W. N. Edeling, D. Coster, O. O. Luk, J. Lakhlili, V. Jancaskas, M Kulczewski, L. Veen, D. Ye, P. Zun, V. Krzhizhanovskaya, A. Hoekstra, D. Crommelin, P. V. Coveney, D. Groen, J. Comput. Sci., 53, 101402 (2021), DOI: 10.1016/j.jocs.2021.101402 The VECMA toolkit enables automated Verification, Validation and Uncertainty Quantification (VVUQ) for complex applications that can be deployed on emerging exascale platforms and provides support for software applications for any domain of interest. The toolkit has four main components including EasyVVUQ for VVUQ workflows, FabSim3 for automation and tool integration, MUSCLE3 for coupling multiscale models and QCG tools to execute application workflows on high performance computing (HPC). A more recent addition to the VECMAtk is EasySurrogate for various types of surrogate methods. In this paper, we present five tutorials from different application domains that apply these VECMAtk components to perform uncertainty quantification analysis, use surrogate models, couple multiscale models and execute sensitivity analysis on HPC. This paper aims to provide hands-on experience for practitioners aiming to test and contrast with their own applications.
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
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
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
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 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.

2020

Title Citation Summary
Computational prediction of GPCR oligomerization A. Townsend-Nicholson, N. Altwaijry, A. Potterton, I. Morao, A. Heifetz, Current Opinion in Structural Biology, 55, 178-184 (2020) DOI: 10.1016/j.sbi.2019.04.005
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
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
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
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
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
TorchMD: A deep learning framework for molecular simulations 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
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
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_11
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
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
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
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
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
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
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
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, Clinical Radiology 75, 1, 78 (2020) DOI: 10.1098/rsos.191752
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
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
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
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
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
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
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
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
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
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.

2019

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/j.sbi.2019.03.021
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
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
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
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
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
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/j.jcp.2019.108905 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/j.sbi.2019.03.021 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/j.sbi.2019.04.005 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).

2018

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, arxiv.org/abs/1801.01174 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/j.sbi.2019.04.005
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/j.sbi.2018.02.004 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.

2017

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, arxiv.org/abs/1712.09168 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, arxiv.org/abs/1710.01054 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.

Acknowledgements

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  • Include the following text for anything published up to September 2019: “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project)”
  • Include the following text for anything published after October 2019: “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823712 (CompBioMed2 project)”

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