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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
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
β-Adrenergic Receptor Stimulation and Alternans in the Border Zone of a Healed Infarct: An ex vivo Study and Computational Investigation of Arrhythmogenesis/td>

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. MI is also associated with remodeling of the sympathetic innervation in the infarct border zone, although how this influences arrhythmogenesis is controversial. We hypothesize that the border zone is most vulnerable to alternans, that β-adrenergic receptor stimulation can suppresses 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.jcim.8b00924 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 C G protein-coupled receptor (GPCR) that is implicated in several CNS disorders making it a popular drug discovery target. Years of research have revealed allosteric mGlu5 ligands showing an unexpected complete switch in functional activity despite only small changes in their chemical structure, resulting in positive allosteric modulators (PAM) or negative allosteric modulators (NAM) for the same scaffold. Up to now, the origins of this effect are not understood, causing difficulties in a drug discovery context. In this work, experimental data was gathered and analyzed alongside docking and Molecular Dynamics (MD) calculations for three sets of PAM and NAM pairs.
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 (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors.
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, In Press 2019 A systematic protocol is applied for the evaluation of free energy calculations with and without replica-exchange. The protocol is based on ensemble avergaing to generate accurate assessments of the uncertainties in the predictions. Comparison is made between FEP+ and TIES -free energy perturbation and thermodynamic integration with enhanced sampling- the latter with and without so-called “enhanced sampling” based on replica-exchange protocols. Standard TIES performs best for a reference set of targets and compounds; no benefits accrue from replica exchange methods. Evaluation of FEP+ and TIES with REST -replica exchange with solute tempering- reveals a systematic and significant underestimation of free energy differences in FEP+, which becomes increasingly large for long duration simulations, is confirmed by extensive analysis of previous publications, and raises a number of questions pertaining to the accuracy of the predictions with the REST technique not hitherto discussed.
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, In Press 2019 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. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent). ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in lactate dehydogenase A, which vary in size, charge and binding mode. Comparisons to experimental results yield excellent statistical rankings across the dataset. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. Normal mode analysis and WSAS do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.
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. This is done by studying a model chaotic dynamical system with a single free parameter β known as the generalized Bernoulli map, many of whose exact properties are known. Much of the structure of the dynamical system is lost in the floating-point representation. For even integer values of the parameter, the long time behaviour is completely wrong, subsuming the known anomalous behaviour for β=2. For non-integer β, relative errors in observables can reach 14%. For odd integer values of β, floating-point results are more accurate, but still produce relative errors two orders of magnitude larger than those attributable to roundoff. The analysis indicates that the pathology described, which cannot be mitigated by increasing the precision of the floating point numbers, is are presentative example of a deeper problem in the computation of expectation values for chaotic systems. The findings sound a warning about the uncritical application of numerical methods in studies of the statistical properties of chaotic dynamical systems, such as are routinely performed throughout computational science, including turbulence and molecular dynamics.
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 (∆ELW) is obtained. These calculated ∆ELW values correlate strongly to the associated experimental residence times of 17 Adenosine A2A receptor ligands, a prototypical class A GPCR.
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. ESMACS is based on MMPBSA and we examinge 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. In this work, 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 In this work, we propose 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 Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models.
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 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. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.
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
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 an ensemble-based free energy approach called thermodynamic integration with enhanced sampling (TIES), which yields accurate, precise, and reproducible binding affinities. These techniques bring us closer to a future where simulation is used to determine drug efficacy for each individual.
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/
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 A computational fluid dynamics study of four highly detailed human left ventricular geometries is presented and compared to smoothed geometries regarding hemodynamic parameters. A porous layer is proposed as a method to incorporate the complex hemodynamics in smoothed models. This study characterizes the dynamics of left ventricular blood flow to examine the impact of trabeculae and papillary muscles using high performance computing (HPC).
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 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.
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 This work explores the influence of the fiber distribution that is used in cardiac simulations. The results show that fibers extracted from experimental data produce functional scores closer to healthy ranges than mathematical models. This study contributes to understand that deep knowledge of 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 inital 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 taking into account the 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 do geomatical 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 bloodflow 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 We present a generic implementation to effectively use supercomputers for (multiscale) models, including biomedical ones. The software simplifies and automates the execution of complex multiscale simulations on High-Performance Computers (HPC), delivering performance benefits from both the end-user and the 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 Cerebral aneurysms can be treated by inserting a flow-diverter in the parent artery. This study proposes simulations involving a coarse grained description of a flow-diverter, based on a screen approach rather than a porous media. Through simulations we can advance our understanding of this aneurysm treatment, leading to improvements and new approaches.
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 deep learning method to infer pharmocophore-like properties in binding pocket of proteins to aid in the drug discovery process. 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 MD simulations and structural biology and a future perspective on the field. This review points out the impact machine learning can have on molecular simulations and all the scientific results coming from them.
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 A quantification of the resolution of in-vivo versus ex-vivo measurements of a model of monomorphic Ventricular Tachycardia in a pig model, particularly regarding scar characterisation. In-vivo clinical measurement techniques over-estimate the scar volume quantified using gadolinium enhanced MRI. Characterization of scar is important to achieve personalized cardiac models.
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 that include structural resolution of around 1 mm^2 cross-section. Wall shear stress, pressure drop and turbulence are some of the quantifications presented. This work shows that neglecting internal structures in cardiac models may lead to inaccurate measures in the cavity.
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, with an error of less than 9% in the given validation setting. HemeLB allows the study of blood flow systems such as aneurysms, by improving our understanding of aneurysms 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.
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 circle of Willis geometry obtained from an MRI scan; we note a strong dependence of the particle density (at a target site) on the strength of the magnetic forcing and the velocity of the background fluid flow. The capability and excellent computational performance of the model allow us to address flow problems that previously could not be approached, and will lead to new a level of understanding.
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 This 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 correct predictions 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 in presented and validated, providing fast predictions with similar accuracy compared with other state-of-the-art methods. KDEEP accelerates the inital stages of drug-discovery, speeding up the time required for novel drug development.
Computational Methods for GPCR Drug Discovery A. Heifetz, Springer, 1705, 2018, 10.1007/978-1-4939-7465-8 This book explores drug interaction with proteins, G protein-coupled receptors (GPCR), that are a common target for pharmaceuticals. The book provides an overview of modern computational strategies and techniques employed in the field of GPCR drug discovery. By exploring and developing such computational methods, we move closer to a future where drug design is regularly informed by simulations.
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 There is a substantial amount of historical ligand binding data available from site-directed mutagenesis (SDM) studies of many different GPCR (G-protein-coupled receptor) subtypes. We describe a protocol by which historic SDM binding data and receptor models may be used together to identify novel key residues for mutagenesis studies.
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, G protein-coupled receptors (GPCR), that are a common target for pharmaceuticals. The chapter explores the modern computational strategy of Hit-to-Lead and Lead Optimization Stages of Structure-Based 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 proposes simulations involving a coarse grained description of a flow-diverter, advancing a previous study by validating the proposed model to complex flow problems and quantifying the benefit of the approach in term of computing speed and accuracy. Through simulations we can advance our understanding of this aneurysm treatment, leading to improvements and new approaches.


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.
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 involving a coarse grained description of a flow-diverter, including patient specific cases. Through simulations we can advance our understanding of this aneurysm treatment, leading to improvements and new approaches.
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, Current platelet function tests are acknowledge to poorly detect various pathology that affect blood. In this paper we propose a methodology which combines clinical images of platelets deposition, a mathematical model of the deposition process, and a HPC machine learning approach to calibrate the model parameters to match the clinical images. From this approach we are able to determine the deposition rates of platelets and detect anomalies. By improving platelet tests through simulation, we can improve the accuracy and capabilities of the medical testing of conditions and diseases.
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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