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Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.

ABSTRACT: Drug-resistant cases of human immunodeficiency virus (HIV) nucleoside reverse transcriptase inhibitors (NRTI) are constantly accumulating due to the frequent mutations of the reverse transcriptase (RT). Predicting the potential drug resistance of HIV-1 NRTIs could provide instructions for the proper clinical use of available drugs. In this study, a novel proteochemometric (PCM) model was constructed to predict the drug resistance between six NRTIs against different variants of RT. Forty-seven dominant mutation sites were screened using the whole protein of HIV-1 RT. Thereafter, the physicochemical properties of the dominant mutation sites can be derived to generate the protein descriptors of RT. Furthermore, by combining the molecular descriptors of NRTIs, PCM modeling can be constructed to predict the inhibition ability between RT variants and NRTIs. The results indicated that our PCM model could achieve a mean AUC value of 0.946 and a mean accuracy of 0.873 on the external validation set. Finally, based on PCM modeling, the importance of features was calculated to reveal the dominant amino acid distribution and mutation patterns on RT, to reflect the characteristics of drug-resistant sequences.

SUBMITTER: Qiu J

PROVIDER: S-EPMC8467226 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundReverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens.Methodology/principal findingsWe applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2=0.89 and Q2ext=0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant.Conclusions/significanceOur results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.

Project description:The development of antiviral drug resistance is an important problem in the treatment of human immunodeficiency virus type 1 (HIV-1) infection. Potent antiretroviral therapy is currently used for treatment, and typically consists of at least two reverse transcriptase (RT) inhibitors. We have previously reported that both drugs and drug-resistant RT mutants can increase virus mutation frequencies. To further assess the contributions of nucleoside RT inhibitors (NRTIs), nonnucleoside RT inhibitors (NNRTIs), and drug-resistant RTs to HIV mutagenesis, a new high-throughput assay system was developed. This assay system was designed to specifically detect frameshift mutations in the luciferase gene in a single virus replication cycle. New drug-resistant RTs were identified that significantly altered virus mutation frequencies. Consistent with our previous observations of NRTIs, abacavir, stavudine, and zalcitabine increased HIV-1 mutation frequencies, supporting the general hypothesis that the NRTIs currently used in antiviral drug therapy increase virus mutation frequencies. Interestingly, similar observations were made with NNRTIs. This is the first report to show that NNRTIs can influence virus mutation frequencies. NNRTI combinations, NRTI-NNRTI combinations, and combinations of drug and drug-resistant RTs led to significant changes in the virus mutation frequencies compared to virus replication of drug-resistant virus in the absence of drug or wild-type virus in the presence of drug. This indicates that combinations of RT drugs or drugs and drug-resistant virus created during the evolution of drug resistance can act together to increase HIV-1 mutation frequencies, which would have important implications for drug therapy regimens. Finally, the influence of drug-resistant RT mutants from CRF01_AE viruses on HIV-1 mutation frequencies was analyzed and it was found that only a highly drug resistant RT led to altered virus mutation frequencies. The results further suggest that high-level drug-resistant RT can significantly influence virus mutation frequencies. A structural model that explains the mutation frequency data is discussed.

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Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.

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