Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London WC1E 6BT, UK.
Protein Eng Des Sel. 2023 Jan 21;36. doi: 10.1093/protein/gzad021.
The Fv region of the antibody (comprising VH and VL domains) is the area responsible for target binding and thus the antibody's specificity. The orientation, or packing, of these two domains relative to each other influences the topography of the Fv region, and therefore can influence the antibody's binding affinity. We present abYpap, an improved method for predicting the packing angle between the VH and VL domains. With the large data set now available, we were able to expand greatly the number of features that could be used compared with our previous work. The machine-learning model was tuned for improved performance using 37 selected residues (previously 13) and also by including the lengths of the most variable 'complementarity determining regions' (CDR-L1, CDR-L2 and CDR-H3). Our method shows large improvements from the previous version, and also against other modeling approaches, when predicting the packing angle.
抗体的 Fv 区域（包含 VH 和 VL 结构域）是负责与靶标结合的区域，因此决定了抗体的特异性。这两个结构域相对彼此的取向或组装方式会影响 Fv 区域的拓扑结构，从而可能影响抗体的结合亲和力。我们提出了 abYpap，这是一种改进的方法，用于预测 VH 和 VL 结构域之间的组装角度。有了现在可用的大型数据集，我们能够与之前的工作相比，大大扩展了可用于的特征数量。使用 37 个选定的残基（之前为 13 个）和包含最可变的“互补决定区”（CDR-L1、CDR-L2 和 CDR-H3）的长度，对机器学习模型进行了调优，以提高性能。与以前的版本相比，我们的方法在预测组装角度方面有了很大的改进，并且也优于其他建模方法。
