Literature DB >> 21714553

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise.

Traian Sulea1, Qizhi Cui, Enrico O Purisima.   

Abstract

Solvated interaction energy (SIE) is an end-point physics-based scoring function for predicting binding affinities from force-field nonbonded interaction terms, continuum solvation, and configurational entropy linear compensation. We tested the SIE function in the Community Structure-Activity Resource (CSAR) scoring challenge consisting of high-resolution cocrystal structures for 343 protein-ligand complexes with high-quality binding affinity data and high diversity with respect to protein targets. Particular emphasis was placed on the sensitivity of SIE predictions to the assignment of protonation and tautomeric states in the complex and the treatment of metal ions near the protein-ligand interface. These were manually curated from an originally distributed CSAR-HiQ data set version, leading to the currently distributed CSAR-NRC-HiQ version. We found that this manual curation was a critical step for accurately testing the performance of the SIE function. The standard SIE parametrization, previously calibrated on an independent data set, predicted absolute binding affinities with a mean-unsigned-error (MUE) of 2.41 kcal/mol for the CSAR-HiQ version, which improved to 1.98 kcal/mol for the upgraded CSAR-NRC-HiQ version. Half-half retraining-testing of SIE parameters on two predefined subsets of CSAR-NRC-HiQ led to only marginal further improvements to an MUE of 1.83 kcal/mol. Hence, we do not recommend altering the current default parameters of SIE at this time. For a sample of SIE outliers, additional calculations by molecular dynamics-based SIE averaging with or without incorporation of ligand strain, by MM-PB(GB)/SA methods with or without entropic estimates, or even by the linear interaction energy (LIE) formalism with an explicit solvent model, did not further improve predictions.

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Year:  2011        PMID: 21714553     DOI: 10.1021/ci2000242

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  18 in total

1.  Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction.

Authors:  Traian Sulea; Hervé Hogues; Enrico O Purisima
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

2.  Molecular simulation methods in drug discovery: a prospective outlook.

Authors:  Xavier Barril; F Javier Luque
Journal:  J Comput Aided Mol Des       Date:  2011-12-08       Impact factor: 3.686

3.  Small-molecule inhibitors of the pseudaminic acid biosynthetic pathway: targeting motility as a key bacterial virulence factor.

Authors:  Robert Ménard; Ian C Schoenhofen; Limei Tao; Annie Aubry; Patrice Bouchard; Christopher W Reid; Paule Lachance; Susan M Twine; Kelly M Fulton; Qizhi Cui; Hervé Hogues; Enrico O Purisima; Traian Sulea; Susan M Logan
Journal:  Antimicrob Agents Chemother       Date:  2014-09-29       Impact factor: 5.191

4.  Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge.

Authors:  Hervé Hogues; Traian Sulea; Enrico O Purisima
Journal:  J Comput Aided Mol Des       Date:  2014-01-29       Impact factor: 3.686

5.  Comparative modeling and molecular dynamics suggest high carboxylase activity of the Cyanobium sp. CACIAM14 RbcL protein.

Authors:  Andrei Santos Siqueira; Alex Ranieri Jerônimo Lima; Leonardo Teixeira Dall'Agnol; Juliana Simão Nina de Azevedo; João Lídio da Silva Gonçalves Vianez; Evonnildo Costa Gonçalves
Journal:  J Mol Model       Date:  2016-03-02       Impact factor: 1.810

6.  CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

Authors:  Heather A Carlson; Richard D Smith; Kelly L Damm-Ganamet; Jeanne A Stuckey; Aqeel Ahmed; Maire A Convery; Donald O Somers; Michael Kranz; Patricia A Elkins; Guanglei Cui; Catherine E Peishoff; Millard H Lambert; James B Dunbar
Journal:  J Chem Inf Model       Date:  2016-05-17       Impact factor: 4.956

Review 7.  Blind prediction of HIV integrase binding from the SAMPL4 challenge.

Authors:  David L Mobley; Shuai Liu; Nathan M Lim; Karisa L Wymer; Alexander L Perryman; Stefano Forli; Nanjie Deng; Justin Su; Kim Branson; Arthur J Olson
Journal:  J Comput Aided Mol Des       Date:  2014-03-05       Impact factor: 3.686

8.  Variability in docking success rates due to dataset preparation.

Authors:  Christopher R Corbeil; Christopher I Williams; Paul Labute
Journal:  J Comput Aided Mol Des       Date:  2012-05-08       Impact factor: 3.686

9.  Multiple Molecular Dynamics Simulations and Energy Analysis Unravel the Dynamic Properties and Binding Mechanism of Mutants HIV-1 Protease with DRV and CA-p2.

Authors:  Ruige Wang; Qingchuan Zheng
Journal:  Microbiol Spectr       Date:  2022-03-23

10.  EGCG reverses human neutrophil elastase-induced migration in A549 cells by directly binding to HNE and by regulating α1-AT.

Authors:  Yilixiati Xiaokaiti; Haoming Wu; Ya Chen; Haopeng Yang; Jianhui Duan; Xin Li; Yan Pan; Lu Tie; Liangren Zhang; Xuejun Li
Journal:  Sci Rep       Date:  2015-07-16       Impact factor: 4.379

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