Predicted resistance to broadly neutralizing antibodies (bnAbs) and associated HIV-1 envelope characteristics among seroconverting adults in Botswana.

We used HIV-1C sequences to predict (in silico) resistance to 33 known broadly neutralizing antibodies (bnAbs) and evaluate the different HIV-1 Env characteristics that may affect virus neutralization. We analyzed proviral sequences from adults with documented HIV-1 seroconversion (N = 140) in Botswana (2013-2018). HIV-1 env sequences were used to predict bnAb resistance using bNAb-ReP, to determine the number of potential N-linked glycosylation sites (PNGS) and evaluate Env variable region characteristics (VC). We also assessed the presence of signature mutations that may affect bnAb sensitivity in vitro. We observe varied results for predicted bnAb resistance among our cohort. 3BNC117 showed high predicted resistance (72%) compared to intermediate levels of resistance to VRC01 (57%). We predict low resistance to PGDM100 and 10-1074 and no resistance to 4E10. No difference was observed in the frequency of PNGS by bNAb susceptibility patterns except for higher number of PNGs in V3 bnAb resistant strains. Associations of VC were observed for V1, V4 and V5 loop length and net charge. We also observed few mutations that have been reported to confer bnAb resistance in vitro. Our results support use of sequence data and machine learning tools to predict the best bnAbs to use within populations.

In Silico Analysis of Hepatitis B Virus Occult Associated Mutations in Botswana Using a Novel Algorithm.

Occult hepatitis B infections (OBI) represent a reservoir of undiagnosed and untreated hepatitis B virus (HBV), hence the need to identify mutations that lead to this phenotype. Functionally characterizing these mutations by in vitro studies is time-consuming and expensive. To bridge this gap, in silico approaches, which predict the effect of amino acid (aa) variants on HBV protein function, are necessary. We developed an algorithm for determining the relevance of OBI-associated mutations using in silico approaches. A 3 kb fragment of subgenotypes A1 and D3 from 24 chronic HBV-infected (CHB) and 24 OBI participants was analyzed. To develop and validate the algorithm, the effects of 68 previously characterized occult-associated mutations were determined using three computational tools: PolyPhen2, SNAP2, and PROVEAN. The percentage of deleterious mutations (with impact on protein function) predicted were 52 (76.5%) by PolyPhen2, 55 (80.9%) by SNAP2, and 65 (95.6%) by PROVEAN. At least two tools correctly predicted 59 (86.8%) mutations as deleterious. To identify OBI-associated mutations exclusive to Botswana, study sequences were compared to CHB sequences from GenBank. Of the 43 OBI-associated mutations identified, 26 (60.5%) were predicted by at least two tools to have an impact on protein function. To our knowledge, this is the first study to use in silico approaches to determine the impact of OBI-associated mutations, thereby identifying potential candidates for functional analysis to facilitate mechanistic studies of the OBI phenotype.