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Updated: Jun 12, 2026

Conformational Evaluation of HIV-1 Trimeric Envelope Glycoproteins Using a Cell-based ELISA Assay

Conformational Evaluation of HIV-1 Trimeric Envelope Glycoproteins Using a Cell-based ELISA Assay

Published on: September 14, 2014

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

Mathilde Foglierini^1,2,3, Pauline Nortier^1,2, Rachel Schelling^1,2

¹Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

Nature Communications

|June 24, 2024

Summary

This summary is machine-generated.

We developed RAIN, a machine learning method to rapidly identify broadly neutralizing antibodies (bNAbs) against HIV-1 from immune repertoires. This accelerates the discovery of potential HIV-1 treatments.

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Area of Science:

Immunology
Computational Biology
Structural Biology

Background:

Broadly neutralizing antibodies (bNAbs) are critical for HIV-1 treatment and prevention.
Current methods for identifying HIV-1 bNAbs from immune repertoires are limited.
Automatic detection of HIV-1 bNAbs is essential for accelerating therapeutic development.

Purpose of the Study:

To develop a computational method for the rapid and accurate identification of HIV-1 broadly neutralizing antibodies (bNAbs).
To facilitate the discovery of novel HIV-1 bNAbs from large-scale immune repertoire sequencing data.

Main Methods:

Developed RAIN (Rapid Automatic Identification of bNAbs), a machine learning approach using sequence-based features.
Applied RAIN to BCR repertoires from HIV-1 immune donors.
Validated identified bNAbs using in vitro neutralization assays and cryo-electron microscopy (cryo-EM).

Main Results:

RAIN accurately predicted HIV-1 bNAbs targeting the CD4-binding site of the HIV-1 envelope glycoprotein.
Successfully identified distinct HIV-1 bNAbs from non-biased BCR repertoires.
Cryo-EM structure elucidated the complex of a identified bNAb with the envelope glycoprotein.

Conclusions:

RAIN provides a straightforward and efficient computational method for HIV-1 bNAb discovery.
The method accelerates the identification of therapeutic antibodies from immune repertoires.
This approach can facilitate the development of new strategies for HIV-1 prevention and treatment.