Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.

Journal: Scientific reports
PMID: 31604961

Abstract

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.

Authors

  • Reda Rawi
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA.
  • Raghvendra Mall
    Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
  • Chen-Hsiang Shen
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • S Katie Farney
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Andrea Shiakolas
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Jing Zhou
  • Halima Bensmail
    Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
  • Tae-Wook Chun
    Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Nicole A Doria-Rose
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Rebecca M Lynch
    Department of Microbiology, Immunology and Tropical Medicine, George Washington University, Washington, DC, USA.
  • John R Mascola
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Peter D Kwong
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, 20892, MD, USA.
  • Gwo-Yu Chuang
    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD, USA.

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