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

Davide Demurtas, Mathilde Foglierini, Philippe Jacquet, Raphael Gottardo, Omar Lweno, Claudia Daubenberger, Laurent Perez, Pauline Nortier, Rachel Schelling, Rahel R Winiger, Sijy O'Dell, Maxmillian Mpina, Yannick D Muller, Constantinos Petrovas, Matthieu Perreau, Nicole A Doria-Rose

Journal: Nature communications 2024;15(1):5339

PMID: 38914562

Free Full Text on Nature Publishing Group View on PubMed

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.

Address: Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Centre for Human Immunology, Lausanne, Switzerland.; Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Centre for Human Immunology, Lausanne, Switzerland.; Scientific Computing and Research Support Unit, University of Lausanne, Lausanne, Switzerland.; Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.; Interdisciplinary center of electron microscopy, CIME, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.; Ifakara Health Institute, Bagamoyo, United Republic of Tanzania.; Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland.; Department of Medical Parasitology and Infection Biology, Clinical Immunology Unit, Swiss Tropical and Public Health Institute, Basel, Switzerland.; University of Basel, Basel, Switzerland.; Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. [email protected].; Centre for Human Immunology, Lausanne, Switzerland. [email protected].

DOI: 10.1038/s41467-024-49676-1

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https://medlineplus.gov/hiv.html

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Research Materials:

https://antibodies.cancer.gov/detail/CPTC-CD4-1

MeSH Terms: HIV-1, Humans, Machine Learning, HIV Antibodies, Cryoelectron Microscopy, Antibodies, Neutralizing, HIV Infections, CD4 Antigens, Amino Acid Sequence, HIV Envelope Protein gp120