Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of our model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

中文翻译：

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通过侧链构象的深度学习改进抗体结构预测

抗体工程在医学中越来越流行，用于开发诊断和免疫疗法。抗体功能在很大程度上依赖于抗原表位通过互补决定区中的环的识别和结合。因此，对这些环进行准确的高分辨率建模对于有效的抗体工程和设计至关重要。深度学习方法先前已被证明可以有效预测被描述为一组残基间距离和方向的抗体骨架结构。然而，抗原结合也取决于表面侧链的特定构象。为了解决这个缺点，我们创建了 DeepSCAb：一种深度学习方法，可以预测抗体可变片段的残基间几何结构以及侧链二面体。该网络只需要序列作为输入，使其特别适用于没有任何已知骨架构象的抗体。Rotamer 预测使用可解释的自我注意层，该层学习识别多个物种的结构保守锚位置。我们评估了我们的模型从诱饵组中区分近原生结构的性能，发现 DeepSCAb 优于缺乏侧链上下文的类似方法。与需要输入骨架结构的替代旋转异构体重新包装方法相比，DeepSCAb 具有竞争力地预测侧链构象。我们的研究结果表明，DeepSCAb 通过准确的侧链建模改进了抗体结构预测，并且适用于抗体-抗原复合物的对接和新的治疗性抗体序列的设计。