Implementation of adaptive integration method for free energy calculations in molecular systems

Department of Physics, University of Idaho, Moscow, Idaho, United States
Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States
Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States
DOI
10.7287/peerj.preprints.27935v1
Subject Areas
Computational Biology, Scientific Computing and Simulation
Keywords
Adaptive integration, Monte Carlo, Free energy, Solvation, Protein, Biomolecule
Copyright
© 2019 Mirabzadeh et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Mirabzadeh CA, Ytreberg FM. 2019. Implementation of adaptive integration method for free energy calculations in molecular systems. PeerJ Preprints 7:e27935v1

Abstract

Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of standard methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than standard methods for these test cases, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.

Author Comment

This is a submission to PeerJ Computer Science for review.

Supplemental Information

Supplemental files

Contains three directories: (1) gmxProgFiles contains source code to build AIM into GROMACS 5.1.4; (2) gmxInputFiles contains input files for GROMACS to reproduce our results; (3) AnalysisFiles contains Jupyter notebook files for analyzing results producing graphs.

DOI: 10.7287/peerj.preprints.27935v1/supp-1