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Updated: Aug 16, 2025
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Attentive Variational Information Bottleneck for TCR-peptide interaction prediction
Filippo Grazioli1, Pierre Machart1, Anja Mösch1
1Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany.
Motivation:
We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides.
Results:
Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences.
Availability And Implementation:
The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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