ArticleScientific reports2023
Predicted resistance to broadly neutralizing antibodies (bnAbs) and associated HIV-1 envelope characteristics among seroconverting adults in Botswana.
Article in Scientific reports, 2023. 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, 2 citations in OpenAlex.
- Article
- HIV-1 virome profiling using HIV-PULSE to guide therapeutic and curative interventions.EBioMedicine · 2026Article
- Resistance to broadly neutralizing antibodies in perinatally infected infants from a Clade C HIV cohort in Mozambique.AIDS (London, England) · 2025Article
- Neutralizing the threat: harnessing broadly neutralizing antibodies against HIV-1 for treatment and prevention.Microbial cell (Graz, Austria) · 2024Article
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18 authors at 3 institutions in 2 countries.
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Abstract
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.
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