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Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates

journal contribution
submitted on 2024-05-27, 07:20 and posted on 2024-05-27, 07:20 authored by Reda Rawi, Raghvendra Mall, Chen-Hsiang Shen, S. Katie Farney, Andrea Shiakolas, Jing Zhou, Halima Bensmail, Tae-Wook Chun, Nicole A. Doria-Rose, Rebecca M. Lynch, John R. Mascola, Peter D. Kwong, Gwo-Yu Chuang

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine – a tree-based machine learning method – enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.

Other Information

Published in: Scientific Reports
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Open Access funding provided by the Qatar National Library.



  • English


Springer Nature

Publication Year

  • 2019

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU