Literature DB >> 26651874

Inexpensive Method for Selecting Receptor Structures for Virtual Screening.

Zunnan Huang1, Chung F Wong2.   

Abstract

This article introduces a screening performance index (SPI) to help select from a number of experimental structures one or a few that are more likely to identify more actives among its top hits from virtual screening of a compound library. It achieved this by docking only known actives to the experimental structures without considering a large number of decoys to reduce computational costs. The SPI is calculated by using the docking energies of the actives to all the receptor structures. We evaluated the performance of the SPI by applying it to study eight protein systems: fatty acid binding protein adipocyte FABP4, serine/threonine-protein kinase BRAF, beta-1 adrenergic receptor ADRB1, TGF-beta receptor type I TGFR1, adenosylhomocysteinase SAHH, thyroid hormone receptor beta-1 THB, phospholipase A2 group IIA PA2GA, and cytochrome P450 3a4 CP3A4. We found that the SPI agreed with the results from other popular performance metrics such as Boltzmann-Enhanced Discrimination Receiver Operator Characteristics (BEDROC), Robust Initial Enhancement (RIE), Area Under Accumulation Curve (AUAC), and Enrichment Factor (EF) but is less expensive to calculate. SPI also performed better than the best docking energy, the molecular volume of the bound ligand, and the resolution of crystal structure in selecting good receptor structures for virtual screening. The implications of these findings were further discussed in the context of ensemble docking, in situations when no experimental structure for the targeted protein was available, or under circumstances when quick choices of receptor structures need to be made before quantitative indexes such as the SPI and BEDROC can be calculated.

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Year:  2015        PMID: 26651874     DOI: 10.1021/acs.jcim.5b00299

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


  13 in total

1.  A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

Authors:  Maria E Mavrogeni; Filippos Pronios; Danae Zareifi; Sofia Vasilakaki; Olivier Lozach; Leonidas Alexopoulos; Laurent Meijer; Vassilios Myrianthopoulos; Emmanuel Mikros
Journal:  Future Med Chem       Date:  2018-10-16       Impact factor: 3.808

2.  A cross docking pipeline for improving pose prediction and virtual screening performance.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2017-08-23       Impact factor: 3.686

3.  Using machine learning to improve ensemble docking for drug discovery.

Authors:  Tanay Chandak; John P Mayginnes; Howard Mayes; Chung F Wong
Journal:  Proteins       Date:  2020-05-25

4.  Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles.

Authors:  Bing Xie; John D Clark; David D L Minh
Journal:  J Chem Inf Model       Date:  2018-08-29       Impact factor: 4.956

5.  Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.

Authors:  Wilfredo Evangelista; Rebecca L Weir; Sally R Ellingson; Jason B Harris; Karan Kapoor; Jeremy C Smith; Jerome Baudry
Journal:  Bioorg Med Chem       Date:  2016-07-29       Impact factor: 3.641

6.  EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery.

Authors:  Tanay Chandak; Chung F Wong
Journal:  Protein Sci       Date:  2021-03-25       Impact factor: 6.725

Review 7.  Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

Authors:  Isabella A Guedes; Felipe S S Pereira; Laurent E Dardenne
Journal:  Front Pharmacol       Date:  2018-09-24       Impact factor: 5.810

8.  Discovery of Potent Disheveled/Dvl Inhibitors Using Virtual Screening Optimized With NMR-Based Docking Performance Index.

Authors:  Kiminori Hori; Kasumi Ajioka; Natsuko Goda; Asako Shindo; Maki Takagishi; Takeshi Tenno; Hidekazu Hiroaki
Journal:  Front Pharmacol       Date:  2018-09-05       Impact factor: 5.810

9.  Exploring the Molecular Basis for Binding of Inhibitors by Threonyl-tRNA Synthetase from Brucella abortus: A Virtual Screening Study.

Authors:  Ming Li; Fang Wen; Shengguo Zhao; Pengpeng Wang; Songli Li; Yangdong Zhang; Nan Zheng; Jiaqi Wang
Journal:  Int J Mol Sci       Date:  2016-07-19       Impact factor: 5.923

10.  Econazole nitrate inhibits PI3K activity and promotes apoptosis in lung cancer cells.

Authors:  Chao Dong; Runxiang Yang; Hongjian Li; Kunbin Ke; Chunxiang Luo; Fang Yang; Xi-Nan Shi; Ying Zhu; Xu Liu; Man-Hon Wong; Guimiao Lin; Xiaomei Wang; Kwong-Sak Leung; Hsiang-Fu Kung; Ceshi Chen; Marie Chia-Mi Lin
Journal:  Sci Rep       Date:  2017-12-21       Impact factor: 4.379

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