Multi-Task Learning with Loop Specific Attention for CDR Structure Prediction

TL;DR

This paper introduces MLSA, a multi-task learning model with loop-specific attention that jointly predicts all three CDR loops in antibodies, significantly improving H3 loop structure prediction accuracy.

Contribution

It is the first to jointly learn all three CDR loops using a novel multi-task strategy with loop-specific attention mechanisms.

Findings

Reduces H3 loop prediction error by at least 19%.

Outperforms baseline strategies on benchmark data.

Provides publicly available implementation for reproducibility.

Abstract

The Complementarity Determining Region (CDR) structure prediction of loops in antibody engineering has gained a lot of attraction by researchers. When designing antibodies, a main challenge is to predict the CDR structure of the H3 loop. Compared with the other CDR loops, that is the H1 and H2 loops, the CDR structure of the H3 loop is more challenging due to its varying length and flexible structure. In this paper, we propose a Multi-task learning model with Loop Specific Attention, namely MLSA. In particular, to the best of our knowledge we are the first to jointly learn the three CDR loops, via a novel multi-task learning strategy. In addition, to account for the structural and functional similarities and differences of the three CDR loops, we propose a loop specific attention mechanism to control the influence of each CDR loop on the training of MLSA. Our experimental evaluation on widely used benchmark data shows that the proposed MLSA method significantly reduces the prediction error of the CDR structure of the H3 loop, by at least 19%, when compared with other baseline strategies. Finally, for reproduction purposes we make the implementation of MLSA publicly available at https://anonymous.4open.science/r/MLSA-2442/.