Literature DB >> 19598265

How to obtain statistically converged MM/GBSA results.

Samuel Genheden1, Ulf Ryde.   

Abstract

The molecular mechanics/generalized Born surface area (MM/GBSA) method has been investigated with the aim of achieving a statistical precision of 1 kJ/mol for the results. We studied the binding of seven biotin analogues to avidin, taking advantage of the fact that the protein is a tetramer with four independent binding sites, which should give the same estimated binding affinities. We show that it is not enough to use a single long simulation (10 ns), because the standard error of such a calculation underestimates the difference between the four binding sites. Instead, it is better to run several independent simulations and average the results. With such an approach, we obtain the same results for the four binding sites, and any desired precision can be obtained by running a proper number of simulations. We discuss how the simulations should be performed to optimize the use of computer time. The correlation time between the MM/GBSA energies is approximately 5 ps and an equilibration time of 100 ps is needed. For MM/GBSA, we recommend a sampling time of 20-200 ps for each separate simulation, depending on the protein. With 200 ps production time, 5-50 separate simulations are required to reach a statistical precision of 1 kJ/mol (800-8000 energy calculations or 1.5-15 ns total simulation time per ligand) for the seven avidin ligands. This is an order of magnitude more than what is normally used, but such a number of simulations is needed to obtain statistically valid results for the MM/GBSA method. (c) 2009 Wiley Periodicals, Inc.

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Year:  2010        PMID: 19598265     DOI: 10.1002/jcc.21366

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  58 in total

1.  Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods.

Authors:  Paulius Mikulskis; Samuel Genheden; Patrik Rydberg; Lars Sandberg; Lars Olsen; Ulf Ryde
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

2.  Absolute free energy of binding of avidin/biotin, revisited.

Authors:  Ignacio J General; Ralitsa Dragomirova; Hagai Meirovitch
Journal:  J Phys Chem B       Date:  2012-02-27       Impact factor: 2.991

3.  In silico profiling and structural insights of zinc metal ion on O6-methylguanine methyl transferase and its interactions using molecular dynamics approach.

Authors:  Marzieh Gharouni; Hamid Mosaddeghi; Jamshid Mehrzad; Ali Es-Haghi; Alireza Motavalizadehkakhky
Journal:  J Mol Model       Date:  2021-01-17       Impact factor: 1.810

4.  Virtual screening using molecular simulations.

Authors:  Tianyi Yang; Johnny C Wu; Chunli Yan; Yuanfeng Wang; Ray Luo; Michael B Gonzales; Kevin N Dalby; Pengyu Ren
Journal:  Proteins       Date:  2011-04-12

5.  Computer-aided Drug Design: Using Numbers to your Advantage.

Authors:  John C Faver; M Nihan Ucisik; Wei Yang; Kenneth M Merz
Journal:  ACS Med Chem Lett       Date:  2013-09-12       Impact factor: 4.345

6.  Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration.

Authors:  Tai-Sung Lee; Yuan Hu; Brad Sherborne; Zhuyan Guo; Darrin M York
Journal:  J Chem Theory Comput       Date:  2017-06-23       Impact factor: 6.006

7.  Assessing protein-ligand binding modes with computational tools: the case of PDE4B.

Authors:  Gülşah Çifci; Viktorya Aviyente; E Demet Akten; Gerald Monard
Journal:  J Comput Aided Mol Des       Date:  2017-05-22       Impact factor: 3.686

8.  Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge.

Authors:  Samuel Genheden; Ana I Cabedo Martinez; Michael P Criddle; Jonathan W Essex
Journal:  J Comput Aided Mol Des       Date:  2014-02-01       Impact factor: 3.686

9.  Effect of explicit water molecules on ligand-binding affinities calculated with the MM/GBSA approach.

Authors:  Paulius Mikulskis; Samuel Genheden; Ulf Ryde
Journal:  J Mol Model       Date:  2014-05-29       Impact factor: 1.810

10.  T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges.

Authors:  Matthew N Davies; Darren R Flower; Kanchan Phadwal; Isabel K Macdonald; Peter V Coveney; Shunzhou Wan
Journal:  Immunome Res       Date:  2010-11-03
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