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RAIN: machine learning-based identification for HIV-1 bNAbs

Author

Listed:
  • Mathilde Foglierini

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology
    Lausanne University Hospital and University of Lausanne)

  • Pauline Nortier

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology)

  • Rachel Schelling

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology)

  • Rahel R. Winiger

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology)

  • Philippe Jacquet

    (University of Lausanne)

  • Sijy O’Dell

    (National Institute of Allergy and Infectious Diseases, National Institutes of Health)

  • Davide Demurtas

    (CIME, Ecole Polytechnique Fédérale de Lausanne)

  • Maxmillian Mpina

    (Ifakara Health Institute)

  • Omar Lweno

    (Ifakara Health Institute)

  • Yannick D. Muller

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology)

  • Constantinos Petrovas

    (Lausanne University Hospital)

  • Claudia Daubenberger

    (Clinical Immunology Unit, Swiss Tropical and Public Health Institute
    University of Basel)

  • Matthieu Perreau

    (Lausanne University Hospital and University of Lausanne)

  • Nicole A. Doria-Rose

    (National Institute of Allergy and Infectious Diseases, National Institutes of Health)

  • Raphael Gottardo

    (Lausanne University Hospital and University of Lausanne)

  • Laurent Perez

    (Lausanne University Hospital and University of Lausanne
    Centre for Human Immunology)

Abstract

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

Suggested Citation

  • Mathilde Foglierini & Pauline Nortier & Rachel Schelling & Rahel R. Winiger & Philippe Jacquet & Sijy O’Dell & Davide Demurtas & Maxmillian Mpina & Omar Lweno & Yannick D. Muller & Constantinos Petrov, 2024. "RAIN: machine learning-based identification for HIV-1 bNAbs," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49676-1
    DOI: 10.1038/s41467-024-49676-1
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