Literature DB >> 12012338

Evolutionary analysis of HIV-1 protease inhibitors: Methods for design of inhibitors that evade resistance.

Daniel Stoffler1, Michel F Sanner, Garrett M Morris, Arthur J Olson, David S Goodsell.   

Abstract

Drug-resistant strains are rapidly selected during AIDS therapy because of the high rate of mutation in HIV. In this report, we present an evolutionary simulation method for analysis of viral mutation and its use for optimization of HIV-1 protease drugs to improve their robustness in the face of resistance mutation. We first present an analysis of the range of resistant mutants that produce viable viruses by using a volume-based viral fitness model. Then, we analyze how this range of mutant proteases allows development of resistance to an optimal inhibitor previously designed by computational coevolution techniques. Finally, we evaluate the resistance patterns of commercially available drugs, and we discuss how resistance might be overcome by optimizing the size of specific side-chains of these inhibitors. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 12012338     DOI: 10.1002/prot.10130

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


  4 in total

1.  Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors.

Authors:  Ge-Fei Hao; Guang-Fu Yang; Chang-Guo Zhan
Journal:  J Phys Chem B       Date:  2010-07-29       Impact factor: 2.991

2.  Interdependence of Inhibitor Recognition in HIV-1 Protease.

Authors:  Janet L Paulsen; Florian Leidner; Debra A Ragland; Nese Kurt Yilmaz; Celia A Schiffer
Journal:  J Chem Theory Comput       Date:  2017-04-11       Impact factor: 6.006

3.  A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein.

Authors:  Jinxin Liu; Jianfeng Pei; Luhua Lai
Journal:  Commun Biol       Date:  2020-01-09

4.  Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

Authors:  Majid Masso; Iosif I Vaisman
Journal:  BMC Genomics       Date:  2013-10-01       Impact factor: 3.969

  4 in total

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