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Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

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Mining antibody functionality via AI-guided structural landscape profiling.

Stanislav S Terekhov¹, Nikita V Ivanisenko², Nan Zhang³

¹Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.

Nature Communications

|March 16, 2026

Summary

This summary is machine-generated.

Researchers developed an AI tool, RBD-AIM, linking antibody sequences to their neutralization function against SARS-CoV-2. This method accelerates the discovery of potent therapeutic antibodies from large datasets.

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Area of Science:

Immunology
Bioinformatics
Artificial Intelligence

Background:

Antibody (Ab) repertoires remain poorly understood despite advances in single-cell screening.
High-throughput sequencing generates vast data, offering potential for deeper insights into Ab functionality.

Purpose of the Study:

To develop an AI-based method linking antibody sequences to their neutralization potential against SARS-CoV-2.
To create a tool for high-throughput prediction of antibody epitopes based on sequence data.
To analyze the functional biodiversity of B cell repertoires.

Main Methods:

Utilized big data from high-throughput sequencing and SARS-CoV-2 pandemic data.
Employed machine learning to process 3D structural data of antibody-receptor binding domain (RBD) complexes.
Developed the RBD-AIM tool for predicting structural antibody epitopes from antibody sequences.
Analyzed native and reconstructed B cell repertoires using single-cell microfluidics and yeast display.

Main Results:

An AI-based method was developed to predict antibody neutralization potential.
The RBD-AIM tool enables high-throughput prediction of antibody epitopes.
Analysis revealed functional biodiversity in B cell repertoires post-vaccination.
The pipeline facilitated the isolation of neutralizing antibodies that protected mice in lethal SARS-CoV-2 infection models.

Conclusions:

An AI-guided sequence-functionality link can be established for antibody discovery.
This approach enables high-throughput discovery and functional analysis of therapeutic antibodies.
The developed pipeline accelerates the identification of potent neutralizing antibodies.