Scripps VIVO scripps research logo

  • Index
  • Log in
  • Home
  • People
  • Organizations
  • Research
  • Events
Search form

Computational coevolution of antiviral drug resistance

Academic Article
uri icon
  • Overview
  • Research
  • Identity
  • Additional Document Info
  • View All
scroll to property group menus

Overview

authors

  • Rosin, C. D.
  • Belew, R. K.
  • Morris, G. M.
  • Olson, Arthur
  • Goodsell, David

publication date

  • 1998

journal

  • Artificial Life  Journal

abstract

  • An understanding of antiviral drug resistance is important in the design of effective drugs. Comprehensive features of the interaction between drug designs and resistance mutations are difficult to study experimentally because of the very large numbers of drugs and mutants involved. We describe a computational framework for studying antiviral drug resistance. Data on HIV-1 protease are used to derive an approximate model that predicts interaction of a wide range of mutant forms of the protease with a broad class of protease inhibitors. An algorithm based on competitive coevolution is used to find highly resistant mutant forms of the protease, and effective inhibitors against such mutants, in the context of the model. We use this method to characterize general features of inhibitors that are effective in overcoming resistance, and to study related issues of selection pathways, cross-resistance, and combination therapies.

subject areas

  • Algorithms
  • Antiviral Agents
  • Computational Biology
  • Drug Design
  • Drug Resistance, Microbial
  • HIV Protease
  • Models, Molecular
scroll to property group menus

Research

keywords

  • HIV
  • coevolution
  • drug resistance
  • genetic algorithms
  • protease inhibitors
  • viruses
scroll to property group menus

Identity

International Standard Serial Number (ISSN)

  • 1064-5462

Digital Object Identifier (DOI)

  • 10.1162/106454698568431

PubMed ID

  • 9798274
scroll to property group menus

Additional Document Info

start page

  • 41

end page

  • 59

volume

  • 4

issue

  • 1

©2021 The Scripps Research Institute | Terms of Use | Powered by VIVO

  • About
  • Contact Us
  • Support