Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates

Reda Rawi, RaghvenPhDa 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

Research output: Contribution to journalArticle


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.

Original languageEnglish
Article number14696
JournalScientific reports
Issue number1
Publication statusPublished - 1 Dec 2019


ASJC Scopus subject areas

  • General

Cite this

Rawi, R., Mall, R., Shen, C. H., Farney, S. K., Shiakolas, A., Zhou, J., Bensmail, H., Chun, T. W., Doria-Rose, N. A., Lynch, R. M., Mascola, J. R., Kwong, P. D., & Chuang, G. Y. (2019). Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates. Scientific reports, 9(1), [14696].