ArticleMolecules (Basel, Switzerland)2022
Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.Journal of chemical information and modeling · 2025Article
- Substitution Models of Protein Evolution with Selection on Enzymatic Activity.Molecular biology and evolution · 2024Article
- Assessing Genomic Mutations in SARS-CoV-2: Potential Resistance to Antiviral Drugs in Viral Populations from Untreated COVID-19 Patients.Microorganisms · 2023Article
- The premise of capsid assembly modulators towards eliminating HBV persistence.Expert opinion on drug discoveryReview
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Authors and funding
6 authors.
Funding
Abstract
Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.
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Registered trials
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