ArticlePLoS computational biology2019
Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Optimizing clinical dosing of combination broadly neutralizing antibodies for HIV prevention.PLoS computational biology · 2022Pooled it
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- Comprehensive mapping of the virus and host factors that guide the paths of HIV-1 escape from a therapeutic.Cell reports · 2026Article
- Genotypic challenges in implementing broadly neutralizing antibody-based long-acting HIV-1 therapies.Communications medicine · 2026Review
- Recurrent mutations drive rapid HIV escape from two broadly neutralizing antibodies in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Translating viral genetic data to PRRSV-2 cross-neutralization using machine learning.Frontiers in immunology · 2026Article
- Germline-Targeting Strategies to Induce bNAbs against HIV-1.Current HIV research · 2026Review
- Comprehensive Mapping of the Virus and Host Factors that Guide the Paths of HIV-1 Escape from a Therapeutic.bioRxiv : the preprint server for biology · 2025Article
- Distinct modes of evolution drive HIV escape from two broadly neutralizing antibodies.bioRxiv : the preprint server for biology · 2025Article
- BRAVE: a highly accurate method for predicting HIV-1 antibody resistance using large language models for proteins.bioRxiv : the preprint server for biology · 2025Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- Data fusion using weakly aligned sources.Journal of the American Statistical Association · 2025Article
- Resistance mutations that distinguish HIV-1 envelopes with discordant VRC01 phenotypes from multi-lineage infections in the HVTN703/HPTN081 trial: implications for cross-resistance.Journal of virology · 2025Article
- Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.PLoS computational biology · 2024Article
- Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.PloS one · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.bioRxiv : the preprint server for biology · 2023Article
- EFFICIENT ESTIMATION OF THE MAXIMAL ASSOCIATION BETWEEN MULTIPLE PREDICTORS AND A SURVIVAL OUTCOME.Annals of statistics · 2023Article
- Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.iScience · 2023Article
- Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies.Nature reviews. Immunology · 2023Review
Corrections and comments
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Authors and funding
20 authors at 8 institutions in 2 countries.
Funding
Abstract
The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.