ArticlePLoS computational biology2017
Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 36 citations in OpenAlex.
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- Genotypic challenges in implementing broadly neutralizing antibody-based long-acting HIV-1 therapies.Communications medicine · 2026Review
- Prediction of HIV-1 sensitivity to broadly neutralizing antibodies using statistical distribution sampling (SDS) technology.BMC bioinformatics · 2026Article
- Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.PLoS computational biology · 2026Article
- 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
- Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.PLoS computational biology · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.PloS one · 2024Article
- Current methods for detecting and assessing HIV-1 antibody resistance.Frontiers in immunology · 2024Review
- Prevalence of resistance-associated viral variants to the HIV-specific broadly neutralising antibody 10-1074 in a UK bNAb-naïve population.Frontiers in immunology · 2024Article
- Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.bioRxiv : the preprint server for biology · 2023Article
- Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.iScience · 2023Article
- Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.Bioinformatics (Oxford, England) · 2022Article
- Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity.Bioinformatics (Oxford, England) · 2021Article
- Neutralizing Activity of Broadly Neutralizing anti-HIV-1 Antibodies against Primary African Isolates.Journal of virology · 2021Article
- Antibodies for Human Immunodeficiency Virus-1 Cure Strategies.The Journal of infectious diseases · 2021Review
- Broadly Neutralizing Antibodies for HIV-1 Prevention.Frontiers in immunology · 2021Review
- Geospatial HIV-1 subtype C gp120 sequence diversity and its predicted impact on broadly neutralizing antibody sensitivity.PloS one · 2021Article
- Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations.Polymers · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient's viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.
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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.