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

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

Authors :
Foglierini, Mathilde
Nortier, Pauline
Schelling, Rachel
Winiger, Rahel R.
Jacquet, Philippe
O'Dell, Sijy
Demurtas, Davide
Mpina, Maxmillian
Lweno, Omar
Muller, Yannick D.
Petrovas, Constantinos
Daubenberger, Claudia
Perreau, Matthieu
Doria-Rose, Nicole A.
Gottardo, Raphael
Perez, Laurent
Source :
Nature Communications; 6/24/2024, Vol. 15 Issue 1, p1-16, 16p
Publication Year :
2024

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. Artificial intelligence holds great promise to improve diagnosis of numerous immune-related or infectious diseases. Here, the authors show that machine learning can be used to identify HIV-1 specific broad neutralising antibody. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Complementary Index
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
178065009
Full Text :
https://doi.org/10.1038/s41467-024-49676-1