PDF(Pubmed)
Abstract：
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
摘要：
结构多样的互补决定区重链3(CDR-H3)环结构的准确预测仍然是抗体建模的主要和长期挑战。这里,我们提出了H3-OPT工具包，用于预测单克隆抗体和纳米抗体的3D结构。H3-OPT将AlphaFold2的优势与预先训练的蛋白质语言模型相结合，并在预测和实验确定的CDR-H3循环之间提供2.24µ平均RMSDCα，从而在我们的非冗余高质量数据集中优于其他当前的计算方法。通过实验求解H3-OPT预测的抗VEGF纳米抗体的三种结构来验证该模型。我们通过分析抗体表面特性和抗体-抗原相互作用来研究H3-OPT的潜在应用。该结构预测工具可用于优化抗体-抗原结合并设计具有生物物理特性的治疗性抗体以用于专门的药物施用途径。
