A hierarchical model of HIV-1 protease drug resistance.

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

A hierarchical model of HIV-1 drug therapy may be used to evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes an atomic representation of drug-protease interaction, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modelling of drug resistance mutations in the face of selection pressures by drug and a mathematical description of viral population dynamics in infected individuals. These techniques have been used for the design of resistance-evading inhibitors by computational coevolution techniques and for the optimisation of existing protease inhibitors for improving their robustness in the face of resistance mutation.

Original languageEnglish (US)
Pages (from-to)3-12
Number of pages10
JournalApplied bioinformatics
Volume1
Issue number1
StatePublished - Jan 1 2002
Externally publishedYes

Fingerprint

drug resistance
Human immunodeficiency virus 1
Drug Resistance
proteinases
Drug therapy
Polyproteins
Population dynamics
Mutation
Population Dynamics
Protease Inhibitors
Drug Interactions
mutation
HIV-1
drugs
Peptide Hydrolases
coevolution
proteinase inhibitors
Pressure
Drug Therapy
drug therapy

All Science Journal Classification (ASJC) codes

  • Agricultural and Biological Sciences(all)
  • Information Systems
  • Computer Science Applications

Cite this

@article{162c364796034948b933d0b1ef80dcf3,
title = "A hierarchical model of HIV-1 protease drug resistance.",
abstract = "A hierarchical model of HIV-1 drug therapy may be used to evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes an atomic representation of drug-protease interaction, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modelling of drug resistance mutations in the face of selection pressures by drug and a mathematical description of viral population dynamics in infected individuals. These techniques have been used for the design of resistance-evading inhibitors by computational coevolution techniques and for the optimisation of existing protease inhibitors for improving their robustness in the face of resistance mutation.",
author = "David Goodsell",
year = "2002",
month = "1",
day = "1",
language = "English (US)",
volume = "1",
pages = "3--12",
journal = "Appl Bioinformatics",
issn = "1175-5636",
publisher = "Adis Press",
number = "1",

}

A hierarchical model of HIV-1 protease drug resistance. / Goodsell, David.

In: Applied bioinformatics, Vol. 1, No. 1, 01.01.2002, p. 3-12.

Research output: Contribution to journalReview article

TY - JOUR

T1 - A hierarchical model of HIV-1 protease drug resistance.

AU - Goodsell, David

PY - 2002/1/1

Y1 - 2002/1/1

N2 - A hierarchical model of HIV-1 drug therapy may be used to evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes an atomic representation of drug-protease interaction, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modelling of drug resistance mutations in the face of selection pressures by drug and a mathematical description of viral population dynamics in infected individuals. These techniques have been used for the design of resistance-evading inhibitors by computational coevolution techniques and for the optimisation of existing protease inhibitors for improving their robustness in the face of resistance mutation.

AB - A hierarchical model of HIV-1 drug therapy may be used to evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes an atomic representation of drug-protease interaction, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modelling of drug resistance mutations in the face of selection pressures by drug and a mathematical description of viral population dynamics in infected individuals. These techniques have been used for the design of resistance-evading inhibitors by computational coevolution techniques and for the optimisation of existing protease inhibitors for improving their robustness in the face of resistance mutation.

UR - http://www.scopus.com/inward/record.url?scp=2442648047&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2442648047&partnerID=8YFLogxK

M3 - Review article

C2 - 15130852

VL - 1

SP - 3

EP - 12

JO - Appl Bioinformatics

JF - Appl Bioinformatics

SN - 1175-5636

IS - 1

ER -