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PLoS Comput Biol. 2015 Apr 20;11(4):e1004249. doi: 10.1371/journal.pcbi.1004249. eCollection 2015 Apr.

Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease.

Author information

1
Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, United States of America; Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, United States of America.
2
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California, United States of America.
3
Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America.
4
Infectious Disease Service, San Antonio Military Medical Center, San Antonio, Texas, United States of America.
5
Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, United States of America; Department of Chemistry, and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, United States of America.

Abstract

While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

PMID:
25894830
PMCID:
PMC4404092
DOI:
10.1371/journal.pcbi.1004249
[Indexed for MEDLINE]
Free PMC Article

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