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
this https URL

本文提出了一个采用多任务学习和循环特定注意的模型，名为 MLSA，用于共同学习三个 CDR 循环，以预测抗体工程中最具挑战性的 H3 环的 CDR 结构，并在广泛使用的基准数据上表明，该方法明显降低了预测误差。