Literature DB >> 12001231

ConsDock: A new program for the consensus analysis of protein-ligand interactions.

Nicodème Paul1, Didier Rognan.   

Abstract

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution. Copyright 2002 Wiley-Liss, Inc.

Mesh:

Substances:

Year:  2002        PMID: 12001231     DOI: 10.1002/prot.10119

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  14 in total

1.  A novel scoring function for molecular docking.

Authors:  A E Muryshev; D N Tarasov; A V Butygin; O Yu Butygina; A B Aleksandrov; S M Nikitin
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2.  Identifying the binding mode of a molecular scaffold.

Authors:  Doron Chema; Doron Eren; Avner Yayon; Amiram Goldblum; Andrea Zaliani
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3.  Exploring the landscape of protein-ligand interaction energy using probabilistic approach.

Authors:  Marcin Pacholczyk; Marek Kimmel
Journal:  J Comput Biol       Date:  2010-11-20       Impact factor: 1.479

4.  Physicochemical and residue conservation calculations to improve the ranking of protein-protein docking solutions.

Authors:  Yuhua Duan; Boojala V B Reddy; Yiannis N Kaznessis
Journal:  Protein Sci       Date:  2005-02       Impact factor: 6.725

5.  A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening.

Authors:  Yoshifumi Fukunishi; Satoru Kubota; Chisato Kanai; Haruki Nakamura
Journal:  J Comput Aided Mol Des       Date:  2006-06-21       Impact factor: 3.686

Review 6.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go.

Authors:  N Moitessier; P Englebienne; D Lee; J Lawandi; C R Corbeil
Journal:  Br J Pharmacol       Date:  2007-11-26       Impact factor: 8.739

Review 7.  Correlation Between Molecular Modelling and Spectroscopic Techniques in Investigation With DNA Binding Interaction of Ruthenium(II) Complexes.

Authors:  B Thulasiram; C Shobha Devi; Yata Praveen Kumar; Rajeshwar Rao Aerva; S Satyanarayana; Penumaka Nagababu
Journal:  J Fluoresc       Date:  2016-12-06       Impact factor: 2.217

Review 8.  Software for molecular docking: a review.

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

9.  Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Authors:  Spencer S Ericksen; Haozhen Wu; Huikun Zhang; Lauren A Michael; Michael A Newton; F Michael Hoffmann; Scott A Wildman
Journal:  J Chem Inf Model       Date:  2017-07-12       Impact factor: 4.956

10.  CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions.

Authors:  Richard D Smith; James B Dunbar; Peter Man-Un Ung; Emilio X Esposito; Chao-Yie Yang; Shaomeng Wang; Heather A Carlson
Journal:  J Chem Inf Model       Date:  2011-08-29       Impact factor: 4.956

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