Literature DB >> 11428927

A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities.

G E Terp1, B N Johansen, I T Christensen, F S Jørgensen.   

Abstract

In this work, eight different scoring functions have been combined with the aim of improving the prediction of protein-ligand binding conformations and affinities. The obtained scores were analyzed using multivariate statistical methods to generate expressions, with the ability (1) to select the best candidate between different docked conformations of an inhibitor (MultiSelect) and (2) to quantify the protein-ligand binding affinity (MultiScore). By use of the docking program GOLD, 40 different inhibitors were docked into the active site of three matrix metalloproteinases (MMP's), yielding a total of 120 enzyme-inhibitor complexes. For each complex, a single conformation of the inhibitor was selected using principal component analysis (PCA) for the scores obtained by the eight functions SCORE, LUDI, GRID, PMF_Score, D_Score, G_Score, ChemScore, and F_Score. Binding affinities were estimated based on partial least-squares projections onto latent structures (PLS) on the eight scores of each selected inhibitor conformation. By use of this procedure, R(2) = 0.78 and Q(2) = 0.78 were obtained when comparing experimental and calculated binding affinities. MultiSelect was evaluated by applying the same method for selecting docked conformations for 18 different protein-ligand complexes of known three-dimensional structure. In all cases, the selected ligand conformations were found to be very similar to the experimentally determined ligand conformations. A more general evaluation of MultiScore was performed using a set of 120 different protein-ligand complexes for which both the three-dimensional structures and the binding affinities were known. This approach allowed an evaluation of MultiScore independently of MultiSelect. The generality of the method was verified by obtaining R(2) = 0.68 and Q(2) = 0.67, when comparing calculated and experimental binding affinities for the 120 X-ray structures. In all cases, LUDI, SCORE, GRID, and F_Score were included as important functions, whereas the fifth function was PMF_Score and ChemScore for the MMP and X-ray models, respectively.

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Year:  2001        PMID: 11428927     DOI: 10.1021/jm001090l

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  14 in total

1.  Docking and scoring of metallo-beta-lactamases inhibitors.

Authors:  Lars Olsen; Ingrid Pettersson; Lars Hemmingsen; Hans-Werner Adolph; Flemming Steen Jørgensen
Journal:  J Comput Aided Mol Des       Date:  2004-04       Impact factor: 3.686

Review 2.  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 3.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Authors:  Johannes Kirchmair; Patrick Markt; Simona Distinto; Gerhard Wolber; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

Review 4.  Receptor-ligand molecular docking.

Authors:  Isabella A Guedes; Camila S de Magalhães; Laurent E Dardenne
Journal:  Biophys Rev       Date:  2013-12-21

5.  Conformationally restricted dipeptide amides as potent and selective neuronal nitric oxide synthase inhibitors.

Authors:  Haitao Ji; José A Gómez-Vidal; Pavel Martasek; Linda J Roman; Richard B Silverman
Journal:  J Med Chem       Date:  2006-10-19       Impact factor: 7.446

6.  Modeling of peroxide activation in artemisinin derivatives by serial docking.

Authors:  Roy J Little; Alexis A Pestano; Zaida Parra
Journal:  J Mol Model       Date:  2009-01-14       Impact factor: 1.810

7.  Evaluation of different virtual screening programs for docking in a charged binding pocket.

Authors:  Wei Deng; Christophe L M J Verlinde
Journal:  J Chem Inf Model       Date:  2008-09-27       Impact factor: 4.956

Review 8.  Advances and challenges in protein-ligand docking.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  Int J Mol Sci       Date:  2010-08-18       Impact factor: 5.923

9.  Design of glycopeptides used to investigate class II MHC binding and T-cell responses associated with autoimmune arthritis.

Authors:  Ida E Andersson; C David Andersson; Tsvetelina Batsalova; Balik Dzhambazov; Rikard Holmdahl; Jan Kihlberg; Anna Linusson
Journal:  PLoS One       Date:  2011-03-15       Impact factor: 3.240

Review 10.  Structure-based virtual screening for drug discovery: a problem-centric review.

Authors:  Tiejun Cheng; Qingliang Li; Zhigang Zhou; Yanli Wang; Stephen H Bryant
Journal:  AAPS J       Date:  2012-01-27       Impact factor: 4.009

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