Literature DB >> 12773034

Comparative evaluation of 11 scoring functions for molecular docking.

Renxiao Wang1, Yipin Lu, Shaomeng Wang.   

Abstract

Eleven popular scoring functions have been tested on 100 protein-ligand complexes to evaluate their abilities to reproduce experimentally determined structures and binding affinities. They include four scoring functions implemented in the LigFit module in Cerius2 (LigScore, PLP, PMF, and LUDI), four scoring functions implemented in the CScore module in SYBYL (F-Score, G-Score, D-Score, and ChemScore), the scoring function implemented in the AutoDock program, and two stand-alone scoring functions (DrugScore and X-Score). These scoring functions are not tested in the context of a particular docking program. Instead, conformational sampling and scoring are separated into two consecutive steps. First, an exhaustive conformational sampling is performed by using the AutoDock program to generate an ensemble of docked conformations for each ligand molecule. This conformational ensemble is required to cover the entire conformational space as much as possible rather than to focus on a few energy minima. Then, each scoring function is applied to score this conformational ensemble to see if it can identify the experimentally observed conformation from all of the other decoys. Among all of the scoring functions under test, six of them, i.e., PLP, F-Score, LigScore, DrugScore, LUDI, and X-Score, yield success rates higher than the AutoDock scoring function. The success rates of these six scoring functions range from 66% to 76% if using root-mean-square deviation < or =2.0 A as the criterion. Combining any two or three of these six scoring functions into a consensus scoring scheme further improves the success rate to nearly 80% or even higher. However, when applied to reproduce the experimentally determined binding affinities of the 100 protein-ligand complexes, only X-Score, PLP, DrugScore, and G-Score are able to give correlation coefficients over 0.50. All of the 11 scoring functions are further inspected by their abilities to construct a descriptive, funnel-shaped energy surface for protein-ligand complexation. The results indicate that X-Score and DrugScore perform better than the other ones at this aspect.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12773034     DOI: 10.1021/jm0203783

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  190 in total

1.  Robust scoring functions for protein-ligand interactions with quantum chemical charge models.

Authors:  Jui-Chih Wang; Jung-Hsin Lin; Chung-Ming Chen; Alex L Perryman; Arthur J Olson
Journal:  J Chem Inf Model       Date:  2011-10-07       Impact factor: 4.956

2.  Identifying the binding mode of a molecular scaffold.

Authors:  Doron Chema; Doron Eren; Avner Yayon; Amiram Goldblum; Andrea Zaliani
Journal:  J Comput Aided Mol Des       Date:  2004-01       Impact factor: 3.686

3.  A collaborative environment for developing and validating predictive tools for protein biophysical characteristics.

Authors:  Michael A Johnston; Damien Farrell; Jens Erik Nielsen
Journal:  J Comput Aided Mol Des       Date:  2012-04-04       Impact factor: 3.686

4.  Molecular docking studies of protein-nucleotide complexes using MOLSDOCK (mutually orthogonal Latin squares DOCK).

Authors:  Shankaran Nehru Viji; Nagarajan Balaji; Namasivayam Gautham
Journal:  J Mol Model       Date:  2012-03-01       Impact factor: 1.810

5.  Discriminating of HMG-CoA reductase inhibitors and decoys using self-organizing maps.

Authors:  Zhi Wang; Aixia Yan
Journal:  Mol Divers       Date:  2010-11-12       Impact factor: 2.943

6.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

7.  Molecular docking simulations for macromolecularly imprinted polymers.

Authors:  David R Kryscio; Yue Shi; Pengyu Ren; Nicholas A Peppas
Journal:  Ind Eng Chem Res       Date:  2011-10-31       Impact factor: 3.720

8.  Molecular modeling on pyruvate phosphate dikinase of Entamoeba histolytica and in silico virtual screening for novel inhibitors.

Authors:  Preyesh Stephen; Ramachandran Vijayan; Audesh Bhat; N Subbarao; R N K Bamezai
Journal:  J Comput Aided Mol Des       Date:  2007-08-21       Impact factor: 3.686

9.  Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks.

Authors:  Lázaro Guillermo Pérez-Montoto; María Auxiliadora Dea-Ayuela; Francisco J Prado-Prado; Francisco Bolas-Fernández; Florencio M Ubeira; Humberto González-Díaz
Journal:  Polymer (Guildf)       Date:  2009-06-03       Impact factor: 4.430

10.  Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

Authors:  Jie Xia; Ermias Lemma Tilahun; Terry-Elinor Reid; Liangren Zhang; Xiang Simon Wang
Journal:  Methods       Date:  2014-12-03       Impact factor: 3.608

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.