Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial

This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.

[1]  B. Zagrovic,et al.  Determination of ensemble-average pairwise root mean-square deviation from experimental B-factors. , 2010, Biophysical journal.

[2]  H. Berendsen.  Simulating the Physical World: Hierarchical Modeling from Quantum Mechanics to Fluid Dynamics , 2007 .

[3]  Berk Hess,et al.  GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers , 2015 .

[4]  Enis Afgan,et al.  BioBlend: automating pipeline analyses within Galaxy and CloudMan , 2013, Bioinform..

[5]  Justin A. Lemkul.  From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0] , 2019, Living Journal of Computational Molecular Science.

[6]  Thomas J Lane,et al.  MDTraj: a modern, open library for the analysis of molecular dynamics trajectories , 2014, bioRxiv.

[7]  Wim F Vranken,et al.  ACPYPE - AnteChamber PYthon Parser interfacE , 2012, BMC Research Notes.

[8]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[9]  L. Pearl,et al.  Structure and mechanism of the Hsp90 molecular chaperone machinery. , 2006, Annual review of biochemistry.

[10]  A. Kirschning,et al.  New geldanamycin derivatives with anti Hsp properties by mutasynthesis. , 2019, Organic & biomolecular chemistry.

[11]  Andreas Prlic,et al.  NGL viewer: web‐based molecular graphics for large complexes , 2018, Bioinform..

[12]  Neal Rosen,et al.  Crystal Structure of an Hsp90–Geldanamycin Complex: Targeting of a Protein Chaperone by an Antitumor Agent , 1997, Cell.

[13]  Maria M. Reif,et al.  New Interaction Parameters for Charged Amino Acid Side Chains in the GROMOS Force Field. , 2012, Journal of chemical theory and computation.

[14]  David L Mobley,et al.  Escaping Atom Types in Force Fields Using Direct Chemical Perception. , 2018, Journal of chemical theory and computation.

[15]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[16]  Oliver Beckstein,et al.  MDAnalysis: A toolkit for the analysis of molecular dynamics simulations , 2011, J. Comput. Chem..

[17]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[18]  Matteo Masetti,et al.  Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics , 2018, J. Chem. Inf. Model..

[19]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[20]  Anthony Bretaudeau,et al.  Community-driven data analysis training for biology , 2017, bioRxiv.

[21]  Justin A. Lemkul.  Pairwise-additive and polarizable atomistic force fields for molecular dynamics simulations of proteins. , 2020, Progress in molecular biology and translational science.

[22]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[23]  Wonpil Im,et al.  Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations , 2020, J. Chem. Inf. Model..

[24]  M. Harvey,et al.  High-throughput molecular dynamics: the powerful new tool for drug discovery. , 2012, Drug discovery today.

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

[26]  Xavier Barril,et al.  rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids , 2014, PLoS Comput. Biol..

[27]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[28]  J. Buchner,et al.  The HSP90 chaperone machinery , 2017, Nature Reviews Molecular Cell Biology.

[29]  David S Wishart,et al.  NMR: prediction of protein flexibility , 2006, Nature Protocols.

[30]  Björn Grüning,et al.  Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE) , 2019, Bioinform..

[31]  Saeed Izadi,et al.  Water models for biomolecular simulations , 2018 .