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/.

翻译：互补决定区（CDR）环区结构预测在抗体工程中已引起研究者的广泛关注。在设计抗体时，主要挑战之一在于预测H3环区的CDR结构。与其他CDR环区（即H1和H2环区）相比，H3环区的CDR结构因其长度可变和柔性结构而更具挑战性。本文提出一种基于循环特异性注意力的多任务学习模型MLSA。具体而言，据我们所知，这是首次通过新型多任务学习策略联合学习三个CDR环区。此外，为考虑三个CDR环区的结构与功能相似性与差异性，我们提出循环特异性注意力机制，以控制各CDR环区对MLSA训练过程的影响。在广泛使用的基准数据集上的实验评估表明，与其他基线策略相比，所提出的MLSA方法将H3环区CDR结构的预测误差降低了至少19%。最后，为便于复现，我们将MLSA的实现代码公开于https://anonymous.4open.science/r/MLSA-2442/。