Dataset Information

Analysis of drug resistance in HIV protease.

ABSTRACT: BACKGROUND:Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. RESULTS:The machine learning produced highly accurate and robust classification of HIV protease resistance. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Generative machine learning models trained on one inhibitor could classify resistance from other inhibitors with varying levels of accuracy. Generally, the accuracy was best when the inhibitors were chemically similar. CONCLUSIONS:Restricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.

SUBMITTER: Pawar SD

PROVIDER: S-EPMC6196403 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Analysis of drug resistance in HIV protease.

Pawar Shrikant D SD Freas Christopher C Weber Irene T IT Harrison Robert W RW

BMC bioinformatics 20181022 Suppl 11

<h4>Background</h4>Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies.<h4>Results</h4>The machine learning produced highly accurate and robust classification of HIV prot ...[more]

PMID: 30343664

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Project description:BackgroundHIV protease inhibitor (PI) therapy results in the rapid selection of drug resistant viral variants harbouring one or two substitutions in the viral protease. To combat PI resistance development, two approaches have been developed. The first is to increase the level of PI in the plasma of the patient, and the second is to develop novel PI with high potency against the known PI-resistant HIV protease variants. Both approaches share the requirement for a considerable increase in the number of protease mutations to lead to clinical resistance, thereby increasing the genetic barrier. We investigated whether HIV could yet again find a way to become less susceptible to these novel inhibitors.Methods and findingsWe have performed in vitro selection experiments using a novel PI with an increased genetic barrier (RO033-4649) and demonstrated selection of three viruses 4- to 8-fold resistant to all PI compared to wild type. These PI-resistant viruses did not have a single substitution in the viral protease. Full genomic sequencing revealed the presence of NC/p1 cleavage site substitutions in the viral Gag polyprotein (K436E and/or I437T/V) in all three resistant viruses. These changes, when introduced in a reference strain, conferred PI resistance. The mechanism leading to PI resistance is enhancement of the processing efficiency of the altered substrate by wild-type protease. Analysis of genotypic and phenotypic resistance profiles of 28,000 clinical isolates demonstrated the presence of these NC/p1 cleavage site mutations in some clinical samples (codon 431 substitutions in 13%, codon 436 substitutions in 8%, and codon 437 substitutions in 10%). Moreover, these cleavage site substitutions were highly significantly associated with reduced susceptibility to PI in clinical isolates lacking primary protease mutations. Furthermore, we used data from a clinical trial (NARVAL, ANRS 088) to demonstrate that these NC/p1 cleavage site changes are associated with virological failure during PI therapy.ConclusionsHIV can use an alternative mechanism to become resistant to PI by changing the substrate instead of the protease. Further studies are required to determine to what extent cleavage site mutations may explain virological failure during PI therapy.

Dataset Information

Analysis of drug resistance in HIV protease.

Publications

Analysis of drug resistance in HIV protease.

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