Literature DB >> 18988271

Prediction and analysis of binding affinities for chemically diverse HIV-1 PR inhibitors by the modified SAFE_p approach.

Miguel Arenas1, M Carmen Villaverde, Fredy Sussman.   

Abstract

One of the biggest challenges in the "in silico" screening of enzyme ligands is to have a protocol that could predict the ligand binding free energies. In our group we have developed a very simple screening function (referred to as solvent accessibility free energy of binding predictor, SAFE_p) which we have applied previously to the study of peptidic HIV-1 protease (HIV-1 PR) inhibitors and later to cyclic urea type HIV-1 PR inhibitors. In this work, we have extended the SAFE_p protocol to a chemically diverse set of HIV-1 PR inhibitors with binding constants that differ by several orders of magnitude. The resulting function is able to reproduce the ranking and in many cases the value of the inhibitor binding affinities for the HIV-1 PR, with accuracy comparable with that of costlier protocols. We also demonstrate that the binding pocket SAFE_p analysis can contribute to the understanding of the physical forces that participate in ligand binding. The analysis tools afforded by our protocol have allowed us to identify an induced fit phenomena mediated by the inhibitor and have demonstrated that larger fragments do not necessarily contribute the most to the binding free energy, an outcome partially brought about by the substantial role the desolvation penalty plays in the energetics of binding. Finally, we have revisited the effect of the Asp dyad protonation state on the predicted binding affinities. 2008 Wiley Periodicals, Inc.

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Year:  2009        PMID: 18988271     DOI: 10.1002/jcc.21147

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


  5 in total

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Journal:  PLoS Comput Biol       Date:  2012-05-31       Impact factor: 4.475

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Journal:  Curr Genomics       Date:  2014-08       Impact factor: 2.236

4.  Computer programs and methodologies for the simulation of DNA sequence data with recombination.

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Journal:  Front Genet       Date:  2013-02-01       Impact factor: 4.599

5.  HIV Protease and Integrase Empirical Substitution Models of Evolution: Protein-Specific Models Outperform Generalist Models.

Authors:  Roberto Del Amparo; Miguel Arenas
Journal:  Genes (Basel)       Date:  2021-12-27       Impact factor: 4.096

  5 in total

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