BioSimSpace: An interoperable Python framework for biomolecular simulation

Biomolecular simulation is a diverse and growing area of research, making important contributions to structural biology and pharmaceutical research (Huggins et al., 2019). Within the community there are a several significant and widely used software packages that have evolved from within various research groups over the past 20 or more years. For example, the molecular dynamics packages AMBER (Case et al., 2005), GROMACS (Abraham et al., 2015), and NAMD (Phillips et al., 2005), which are all capable of running biomolecular simulations for systems consisting of hundreds of thousands of atoms and can be run on hardware ranging from laptops, to graphics processing units (GPUs), to the latest high-performance computing clusters. Since different software packages were developed independently, interoperability between them is poor. In large part this is the result of major differences in the supported file formats, which makes it difficult to translate the inputs and outputs of one program to another. As a consequence, expertise in one package doesn’t immediately apply to another, making it hard to share methodology and knowledge between different research communities, as evidenced, for instance, by a recent study on reproducibility of relative hydration free energies across simulation packages (Loeffler et al., 2018). The issue is compounded by the increasing use of biomolecular simulations as components of larger scientific workflows for bioengineering or computer-aided drug design purposes. A lack of interoperability leads to brittle workflows, poor reproducibility, and lock in to specific software that hinders dissemination of biomolecular simulation methodologies to other communities.

[1]  Massimiliano Bonomi,et al.  PLUMED 2: New feathers for an old bird , 2013, Comput. Phys. Commun..

[2]  Takeshi Kawabata,et al.  Build-Up Algorithm for Atomic Correspondence between Chemical Structures , 2011, J. Chem. Inf. Model..

[3]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

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

[5]  Julien Michel,et al.  Reproducibility of Free Energy Calculations across Different Molecular Simulation Software Packages. , 2018, Journal of chemical theory and computation.

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

[7]  Jian Yin,et al.  Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset , 2016, bioRxiv.

[8]  Bryce K. Allen,et al.  Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations , 2017, J. Chem. Inf. Model..

[9]  Jan Rezác,et al.  Cuby: An integrative framework for computational chemistry , 2016, J. Comput. Chem..

[10]  Richard H. Henchman,et al.  Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity , 2018, WIREs Computational Molecular Science.

[11]  Hannes H. Loeffler,et al.  FESetup: Automating Setup for Alchemical Free Energy Simulations , 2015, J. Chem. Inf. Model..

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

[13]  Oliver Beckstein,et al.  MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations , 2016, SciPy.

[14]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[15]  Vijay S. Pande,et al.  OpenMM 7: Rapid development of high performance algorithms for molecular dynamics , 2016, bioRxiv.