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LigDockCSA: Protein-Ligand Docking Using Conformational Space Annealing

Cited 32 time in Web of Science Cited 34 time in Scopus
Authors

Shin, Woong-Hee; Heo, Lim; Lee, Ju Yong; Ko, Jun Su; Seok, Chaok; Lee, Joo Young

Issue Date
2011-11
Publisher
John Wiley & Sons Inc.
Citation
Journal of Computational Chemistry, Vol.32 No.15, pp.3226-3232
Abstract
Protein-ligand docking techniques are one of the essential tools for structure-based drug design. Two major components of a successful docking program are an efficient search method and an accurate scoring function. In this work, a new docking method called LigDockCSA is developed by using a powerful global optimization technique, conformational space annealing (CSA), and a scoring function that combines the AutoDock energy and the piecewise linear potential (PLP) torsion energy. It is shown that the CSA search method can find lower energy binding poses than the Lamarckian genetic algorithm of AutoDock. However, lower-energy solutions CSA produced with the AutoDock energy were often less native-like. The loophole in the AutoDock energy was fixed by adding a torsional energy term, and the CSA search on the refined energy function is shown to improve the docking performance. The performance of LigDockCSA was tested on the Astex diverse set which consists of 85 protein-ligand complexes. LigDockCSA finds the best scoring poses within 2 angstrom root-mean-square deviation (RMSD) from the native structures for 84.7% of the test cases, compared to 81.7% for AutoDock and 80.5% for GOLD. The results improve further to 89.4% by incorporating the conformational entropy. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 3226-3232, 2011
ISSN
0192-8651
URI
https://hdl.handle.net/10371/201545
DOI
https://doi.org/10.1002/jcc.21905
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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