Literature DB >> 22961860

FIPSDock: a new molecular docking technique driven by fully informed swarm optimization algorithm.

Yu Liu1, Lei Zhao, Wentao Li, Dongyu Zhao, Miao Song, Yongliang Yang.   

Abstract

The accurate prediction of protein-ligand binding is of great importance for rational drug design. We present herein a novel docking algorithm called as FIPSDock, which implements a variant of the Fully Informed Particle Swarm (FIPS) optimization method and adopts the newly developed energy function of AutoDock 4.20 suite for solving flexible protein-ligand docking problems. The search ability and docking accuracy of FIPSDock were first evaluated by multiple cognate docking experiments. In a benchmarking test for 77 protein/ligand complex structures derived from GOLD benchmark set, FIPSDock has obtained a successful predicting rate of 93.5% and outperformed a few docking programs including particle swarm optimization (PSO)@AutoDock, SODOCK, AutoDock, DOCK, Glide, GOLD, FlexX, Surflex, and MolDock. More importantly, FIPSDock was evaluated against PSO@AutoDock, SODOCK, and AutoDock 4.20 suite by cross-docking experiments of 74 protein-ligand complexes among eight protein targets (CDK2, ESR1, F2, MAPK14, MMP8, MMP13, PDE4B, and PDE5A) derived from Sutherland-crossdock-set. Remarkably, FIPSDock is superior to PSO@AutoDock, SODOCK, and AutoDock in seven out of eight cross-docking experiments. The results reveal that FIPS algorithm might be more suitable than the conventional genetic algorithm-based algorithms in dealing with highly flexible docking problems.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22961860     DOI: 10.1002/jcc.23108

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


  12 in total

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Review 2.  Software for molecular docking: a review.

Authors:  Nataraj S Pagadala; Khajamohiddin Syed; Jack Tuszynski
Journal:  Biophys Rev       Date:  2017-01-16

3.  A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy.

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Journal:  J Mol Model       Date:  2014-06-17       Impact factor: 1.810

4.  DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites.

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Journal:  PLoS One       Date:  2015-07-16       Impact factor: 3.240

5.  A Hybrid Cuckoo Search and Differential Evolution Approach to Protein⁻Ligand Docking.

Authors:  Hang Lin; Shirley W I Siu
Journal:  Int J Mol Sci       Date:  2018-10-15       Impact factor: 5.923

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Authors:  Yue Pang; Meng Gou; Kai Yang; Jiali Lu; Yinglun Han; Hongming Teng; Changzhi Li; Haina Wang; Caigang Liu; Kejia Zhang; Yongliang Yang; Qingwei Li
Journal:  Cell Commun Signal       Date:  2019-05-27       Impact factor: 5.712

7.  Energy Landscape Topography Reveals the Underlying Link Between Binding Specificity and Activity of Enzymes.

Authors:  Wen-Ting Chu; Jin Wang
Journal:  Sci Rep       Date:  2016-06-14       Impact factor: 4.379

8.  Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Authors:  Vincent Zoete; Thierry Schuepbach; Christophe Bovigny; Prasad Chaskar; Antoine Daina; Ute F Röhrig; Olivier Michielin
Journal:  J Comput Chem       Date:  2015-11-12       Impact factor: 3.376

Review 9.  Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface.

Authors:  Nandhini Rajagopal; Flaviyan Jerome Irudayanathan; Shikha Nangia
Journal:  Int J Mol Sci       Date:  2019-11-08       Impact factor: 5.923

10.  Checkpoint therapeutic target database (CKTTD): the first comprehensive database for checkpoint targets and their modulators in cancer immunotherapy.

Authors:  Yixiao Zhang; Yuan Yao; Peng Chen; Yu Liu; Hao Zhang; He Liu; Yu Liu; Hong Xu; Xibao Tian; Zichu Wang; Peng Chu; Dongyu Zhao; Hongxu Liu; Chenlei Zhang; Shuyuan Chen; Yuhong Zhao; Caigang Liu; Yongliang Yang
Journal:  J Immunother Cancer       Date:  2020-10       Impact factor: 13.751

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