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

Mathilde Foglierini, Pauline Nortier, Rachel Schelling, Rahel R. Winiger, Philippe Jacquet, Sijy O’Dell, Davide Demurtas, Maxmillian Mpina, Omar Lweno, Yannick D. Muller, Constantinos Petrovas, Claudia Daubenberger, Matthieu Perreau, Nicole A. Doria-Rose, Raphael Gottardo and Laurent Perez ()
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Mathilde Foglierini: Lausanne University Hospital and University of Lausanne
Pauline Nortier: Lausanne University Hospital and University of Lausanne
Rachel Schelling: Lausanne University Hospital and University of Lausanne
Rahel R. Winiger: Lausanne University Hospital and University of Lausanne
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
Constantinos Petrovas: Lausanne University Hospital
Claudia Daubenberger: Clinical Immunology Unit, Swiss Tropical and Public Health Institute
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

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49676-1

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