Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2

• Authors: Dharmeshkumar Patel, Emory University; Suzane K Ono, Emory University; Leda Bassit, Emory University; Kiran Verma, Emory University; Franck Amblard, Emory University; Raymond Schinazi, Emory University
• Journal Title: MOLECULES
• Volume: Volume 27, Number 17
• Publisher: MDPI | 2022-09-01
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3390/molecules27175413
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Life Sciences & Biomedicine; Physical Sciences; Biochemistry & Molecular Biology; Chemistry, Multidisciplinary; Chemistry; HIV; HBV; SARS-CoV-2; 3CLpro; RT; capsid; drug resistance; mutation; residue scanning; MM-GBSA; emtricitabine; nirmatrelvir; HUMAN IMMUNODEFICIENCY VIRUSES; WILD-TYPE; INHIBITORS; EMTRICITABINE; MECHANISMS; GENERATION; STABILITY; BINDING

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.