Literature DB >> 33457645

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

Christopher A Mirabzadeh1, F Marty Ytreberg1,2,3.   

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 other 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 other tested methods for these systems, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.

Entities:  

Keywords:  Adaptive integration; Biomolecule; Computational Biology; Free energy; Monte Carlo; Protein; Scientific Computing and Simulation; Solvation

Year:  2020        PMID: 33457645      PMCID: PMC7808261          DOI: 10.7717/peerj-cs.264

Source DB:  PubMed          Journal:  PeerJ Comput Sci        ISSN: 2376-5992


  28 in total

1.  Adaptive integration method for Monte Carlo simulations.

Authors:  Marc Fasnacht; Robert H Swendsen; John M Rosenberg
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2004-05-24

2.  Statistically optimal analysis of samples from multiple equilibrium states.

Authors:  Michael R Shirts; John D Chodera
Journal:  J Chem Phys       Date:  2008-09-28       Impact factor: 3.488

3.  Reducing the bias and uncertainty of free energy estimates by using regression to fit thermodynamic integration data.

Authors:  Conrad Shyu; F Marty Ytreberg
Journal:  J Comput Chem       Date:  2009-11-15       Impact factor: 3.376

Review 4.  Alchemical free energy methods for drug discovery: progress and challenges.

Authors:  John D Chodera; David L Mobley; Michael R Shirts; Richard W Dixon; Kim Branson; Vijay S Pande
Journal:  Curr Opin Struct Biol       Date:  2011-02-23       Impact factor: 6.809

5.  Guidelines for the analysis of free energy calculations.

Authors:  Pavel V Klimovich; Michael R Shirts; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2015-03-26       Impact factor: 3.686

Review 6.  Lambda-dynamics free energy simulation methods.

Authors:  Jennifer L Knight; Charles L Brooks
Journal:  J Comput Chem       Date:  2009-08       Impact factor: 3.376

7.  Enhanced ligand sampling for relative protein-ligand binding free energy calculations.

Authors:  Joseph W Kaus; J Andrew McCammon
Journal:  J Phys Chem B       Date:  2015-05-08       Impact factor: 2.991

8.  Changing folding and binding stability in a viral coat protein: a comparison between substitutions accessible through mutation and those fixed by natural selection.

Authors:  Craig R Miller; Kuo Hao Lee; Holly A Wichman; F Marty Ytreberg
Journal:  PLoS One       Date:  2014-11-18       Impact factor: 3.240

9.  DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity.

Authors:  Haiping Zhang; Linbu Liao; Konda Mani Saravanan; Peng Yin; Yanjie Wei
Journal:  PeerJ       Date:  2019-07-25       Impact factor: 2.984

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