Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5–10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.

[1]  Supot Hannongbua,et al.  Accurate prediction of protonation state as a prerequisite for reliable MM‐PB(GB)SA binding free energy calculations of HIV‐1 protease inhibitors , 2008, J. Comput. Chem..

[2]  Tingjun Hou,et al.  Molecular dynamics and free energy studies on the wild-type and double mutant HIV-1 protease complexed with amprenavir and two amprenavir-related inhibitors: mechanism for binding and drug resistance. , 2007, Journal of medicinal chemistry.

[3]  W. Rutter,et al.  Structural origins of substrate discrimination in trypsin and chymotrypsin. , 1995, Biochemistry.

[4]  Frederick P. Brooks,et al.  Project GROPEHaptic displays for scientific visualization , 1990, SIGGRAPH.

[5]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[6]  Rosalind J Allen,et al.  Forward flux sampling for rare event simulations , 2009, Journal of physics. Condensed matter : an Institute of Physics journal.

[7]  Todd J Martínez,et al.  Ab initio interactive molecular dynamics on graphical processing units (GPUs). , 2015, Journal of chemical theory and computation.

[8]  Oussama Metatla,et al.  Sampling molecular conformations and dynamics in a multiuser virtual reality framework , 2018, Science Advances.

[9]  Ron O. Dror,et al.  Molecular Dynamics Simulation for All , 2018, Neuron.

[10]  A. Caflisch,et al.  Molecular dynamics in drug design. , 2015, European journal of medicinal chemistry.

[11]  A. Cavalli,et al.  Dynamic Docking: A Paradigm Shift in Computational Drug Discovery , 2017, Molecules.

[12]  M. Karplus,et al.  Molecular dynamics and protein function. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Sébastien Limet,et al.  Interactive Molecular Dynamics: Scaling up to Large Systems , 2013, ICCS.

[14]  Robert V. Swift,et al.  Mechanism of 150-cavity formation in influenza neuraminidase , 2011, Nature communications.

[15]  Klaus Schulten,et al.  A system for interactive molecular dynamics simulation , 2001, I3D '01.

[16]  S. Naem The role of neuraminidase inhibitors in the treatment and prevention of influenza , 2001, Journal of Biomedicine and Biotechnology.

[17]  Gerhard Wolber,et al.  The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. , 2015, Drug discovery today.

[18]  W. E,et al.  Finite temperature string method for the study of rare events. , 2002, Journal of Physical Chemistry B.

[19]  Klaus Schulten,et al.  Adaptive Multilevel Splitting Method for Molecular Dynamics Calculation of Benzamidine-Trypsin Dissociation Time. , 2016, Journal of chemical theory and computation.

[20]  David Chandler,et al.  Transition path sampling: throwing ropes over rough mountain passes, in the dark. , 2002, Annual review of physical chemistry.

[21]  Parimal Kar,et al.  Energetic basis for drug resistance of HIV-1 protease mutants against amprenavir , 2012, Journal of Computer-Aided Molecular Design.

[22]  S. Bhakat,et al.  Flap Dynamics in Aspartic Proteases: A Computational Perspective , 2016, Chemical biology & drug design.

[23]  Rommie E. Amaro,et al.  Ensemble Docking in Drug Discovery. , 2018, Biophysical journal.

[24]  George F. Gao,et al.  Induced opening of influenza virus neuraminidase N2 150-loop suggests an important role in inhibitor binding , 2013, Scientific Reports.

[25]  Qinggang Zhang,et al.  Revealing Origin of Decrease in Potency of Darunavir and Amprenavir against HIV-2 relative to HIV-1 Protease by Molecular Dynamics Simulations , 2014, Scientific Reports.

[26]  G. Klebe,et al.  Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes , 2018, Nature Communications.

[27]  Mark C. Surles,et al.  Sculpting proteins interactively: Continual energy minimization embedded in a graphical modeling system , 1994, Protein science : a publication of the Protein Society.

[28]  W G Laver,et al.  Influenza neuraminidase inhibitors possessing a novel hydrophobic interaction in the enzyme active site: design, synthesis, and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity. , 1997, Journal of the American Chemical Society.

[29]  Yoshihiro Kawaoka,et al.  Influenza: lessons from past pandemics, warnings from current incidents , 2005, Nature Reviews Microbiology.

[30]  Daniel Pargman,et al.  Computing within limits , 2018, Commun. ACM.

[31]  Chi‐Huey Wong,et al.  HIV-1 protease: mechanism and drug discovery. , 2003, Organic & biomolecular chemistry.

[32]  Manthos G. Papadopoulos,et al.  Computational Studies of Darunavir into HIV-1 Protease and DMPC Bilayer: Necessary Conditions for Effective Binding and the Role of the Flaps , 2012, J. Chem. Inf. Model..

[33]  Mark E Tuckerman,et al.  Molecular dynamics study of the connection between flap closing and binding of fullerene-based inhibitors of the HIV-1 protease. , 2003, Biochemistry.

[34]  S. Doerr,et al.  On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. , 2014, Journal of chemical theory and computation.

[35]  F. Noé,et al.  Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models , 2015, Nature Communications.

[36]  Y. Yonetani Water access and ligand dissociation at the binding site of proteins. , 2018, The Journal of chemical physics.

[37]  K. Schulten,et al.  Mechanisms of selectivity in channels and enzymes studied with interactive molecular dynamics. , 2003, Biophysical journal.

[38]  D. M. Ryan,et al.  Rational design of potent sialidase-based inhibitors of influenza virus replication , 1993, Nature.

[39]  Frederick P. Brooks,et al.  Force display performs better than visual display in a simple 6-D docking task , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[40]  Adrian J Mulholland,et al.  An open-source multi-person virtual reality framework for interactive molecular dynamics: from quantum chemistry to drug binding , 2019, The Journal of chemical physics.

[41]  R. Stroud,et al.  Structure and specific binding of trypsin: comparison of inhibited derivatives and a model for substrate binding. , 1974, Journal of molecular biology.

[42]  J. Tuszynski,et al.  Software for molecular docking: a review , 2017, Biophysical Reviews.

[43]  Kent R. Wilson,et al.  Computing with feeling , 1977, Comput. Graph..

[44]  M. Levitt A simplified representation of protein conformations for rapid simulation of protein folding. , 1976, Journal of molecular biology.

[45]  A. Roitberg,et al.  pH-REMD simulations indicate that the catalytic aspartates of HIV-1 protease exist primarily in a monoprotonated state. , 2014, The journal of physical chemistry. B.

[46]  Amedeo Caflisch,et al.  Protein structure-based drug design: from docking to molecular dynamics. , 2018, Current opinion in structural biology.

[47]  K. Hertogs,et al.  Binding Kinetics of Darunavir to Human Immunodeficiency Virus Type 1 Protease Explain the Potent Antiviral Activity and High Genetic Barrier , 2007, Journal of Virology.

[48]  David R. Glowacki,et al.  Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality , 2019, The journal of physical chemistry. A.

[49]  William E. Hart,et al.  Robust Proofs of NP-Hardness for Protein Folding: General Lattices and Energy Potentials , 1997, J. Comput. Biol..

[50]  E. Baker,et al.  Hydrogen bonding in globular proteins. , 1984, Progress in biophysics and molecular biology.

[51]  Simon McIntosh-Smith,et al.  Adaptive free energy sampling in multidimensional collective variable space using boxed molecular dynamics. , 2016, Faraday discussions.

[52]  J. Richardson,et al.  Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. , 1999, Journal of molecular biology.

[53]  M. Murcko,et al.  Crystal Structure of HIV-1 Protease in Complex with Vx-478, a Potent and Orally Bioavailable Inhibitor of the Enzyme , 1995 .

[54]  Fabio Polticelli,et al.  Structural determinants of trypsin affinity and specificity for cationic inhibitors , 1999, Protein science : a publication of the Protein Society.

[55]  Ashima Bagaria,et al.  Molecular Dynamics Simulation of Proteins: A Brief Overview , 2014 .

[56]  Stéphane Redon,et al.  Interactive chemical reactivity exploration. , 2014, Chemphyschem : a European journal of chemical physics and physical chemistry.

[57]  Rommie E. Amaro,et al.  SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin-Benzamidine Binding. , 2017, The journal of physical chemistry. B.

[58]  M. Karplus,et al.  Dynamics of folded proteins , 1977, Nature.

[59]  Gregory R Bowman,et al.  FAST Conformational Searches by Balancing Exploration/Exploitation Trade-Offs. , 2015, Journal of chemical theory and computation.

[60]  Victor Guallar,et al.  Computational Prediction of HIV-1 Resistance to Protease Inhibitors , 2016, J. Chem. Inf. Model..

[61]  Markku Hämäläinen,et al.  Elucidation of HIV-1 protease resistance by characterization of interaction kinetics between inhibitors and enzyme variants. , 2003, Antiviral research.

[62]  G. de Fabritiis,et al.  Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations , 2011, Proceedings of the National Academy of Sciences.

[63]  Mark von Itzstein,et al.  The war against influenza: discovery and development of sialidase inhibitors. , 2007, Nature reviews. Drug discovery.

[64]  Christopher J. Woods,et al.  Computational Assay of H7N9 Influenza Neuraminidase Reveals R292K Mutation Reduces Drug Binding Affinity , 2013, Scientific Reports.