Literature DB >> 10899786

Similarity-driven flexible ligand docking.

X Fradera1, R M Knegtel, J Mestres.   

Abstract

A similarity-driven approach to flexible ligand docking is presented. Given a reference ligand or a pharmacophore positioned in the protein active site, the method allows inclusion of a similarity term during docking. Two different algorithms have been implemented, namely, a similarity-penalized docking (SP-DOCK) and a similarity-guided docking (SG-DOCK). The basic idea is to maximally exploit the structural information about the ligand binding mode present in cases where ligand-bound protein structures are available, information that is usually ignored in standard docking procedures. SP-DOCK and SG-DOCK have been derived as modified versions of the program DOCK 4.0, where the similarity program MIMIC acts as a module for the calculation of similarity indices that correct docking energy scores at certain steps of the calculation. SP-DOCK applies similarity corrections to the set of ligand orientations at the end of the ligand incremental construction process, penalizing the docking energy and, thus, having only an effect on the relative ordering of the final solutions. SG-DOCK applies similarity corrections throughout the entire ligand incremental construction process, thus affecting not only the relative ordering of solutions but also actively guiding the ligand docking. The performance of SP-DOCK and SG-DOCK for binding mode assessment and molecular database screening is discussed. When applied to a set of 32 thrombin ligands for which crystal structures are available, SG-DOCK improves the average RMSD by ca. 1 A when compared with DOCK. When those 32 thrombin ligands are included into a set of 1,000 diverse molecules from the ACD, DIV, and WDI databases, SP-DOCK significantly improves the retrieval of thrombin ligands within the first 10% of each of the three databases with respect to DOCK, with minimal additional computational cost. In all cases, comparison of SP-DOCK and SG-DOCK results with those obtained by DOCK and MIMIC is performed.

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Year:  2000        PMID: 10899786     DOI: 10.1002/1097-0134(20000901)40:4<623::aid-prot70>3.0.co;2-i

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


  16 in total

1.  Flexible docking under pharmacophore type constraints.

Authors:  Sally A Hindle; Matthias Rarey; Christian Buning; Thomas Lengaue
Journal:  J Comput Aided Mol Des       Date:  2002-02       Impact factor: 3.686

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.  Improving molecular docking through eHiTS' tunable scoring function.

Authors:  Orr Ravitz; Zsolt Zsoldos; Aniko Simon
Journal:  J Comput Aided Mol Des       Date:  2011-11-11       Impact factor: 3.686

4.  Influence of conformation on the representation of small flexible molecules at low resolution: alignment of endothiapepsin ligands.

Authors:  Laurence Leherte; Nathalie Meurice; Daniel P Vercauteren
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

5.  Unsupervised guided docking of covalently bound ligands.

Authors:  Xavier Fradera; Jasmit Kaur; Jordi Mestres
Journal:  J Comput Aided Mol Des       Date:  2004-10       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

7.  An integrated approach to knowledge-driven structure-based virtual screening.

Authors:  Angela M Henzler; Sascha Urbaczek; Matthias Hilbig; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2014-07-04       Impact factor: 3.686

8.  A pose prediction approach based on ligand 3D shape similarity.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2016-07-05       Impact factor: 3.686

9.  Interactions of peptide mimics of hyaluronic acid with the receptor for hyaluronan mediated motility (RHAMM).

Authors:  Michael R Ziebell; Glenn D Prestwich
Journal:  J Comput Aided Mol Des       Date:  2004-10       Impact factor: 3.686

10.  Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening.

Authors:  Maria I Zavodszky; Paul C Sanschagrin; Rajesh S Korde; Leslie A Kuhn
Journal:  J Comput Aided Mol Des       Date:  2002-12       Impact factor: 3.686

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