2024-10-29 ジョージア工科大学
<関連情報>
- https://research.gatech.edu/new-ai-tool-identifies-better-antibody-therapies
- https://www.pnas.org/doi/10.1073/pnas.2410529121
抗原抗体相互作用のディープラーニングによる予測を改善 Improved deep learning prediction of antigen–antibody interactions
Mu Gao and Jeffrey Skolnick
Proceedings of the National Academy of Sciences Published:October 3, 2024
DOI:https://doi.org/10.1073/pnas.2410529121
Significance
Accurately predicting antibody–antigen interactions, which are central to the adaptive immune response, is a daunting task. This study explores the potential of a deep learning approach for computationally predicting these interactions. Using the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a test case, we demonstrate the capability of this computational approach to predict interactions between antibodies and various epitopes on the surface of the antigen. One particularly promising strategy involves using antibody sequences collected from B cell sequencing. The encouraging findings from this approach have significant implications for practical applications to antibody development.
Abstract
Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.
