AntiFold：使用逆向折叠改进的基于抗体结构的设计
AntiFold: Improved antibody structure-based design using inverse folding
AI总结：
AntiFold 是基于 ESM-IF1 微调的抗体特异逆向折叠模型，在序列恢复和零样本结合亲和力预测上优于现有工具，可用于指导保留结构的抗体优化。
摘要：
抗体的设计与优化需要在多种性质之间取得复杂的平衡。蛋白质逆向折叠模型能够生成折叠成相同结构的多样化序列，是在抗体设计过程中维持结构完整性的有前景的工具。在此，我们提出了 AntiFold，一种抗体特异的逆向折叠模型，该模型基于已解析和预测的抗体结构对 ESM-IF1 进行微调。AntiFold 在互补决定区的序列恢复方面优于现有的逆向折叠工具，其设计的序列与已解析的对应结构表现出高度结构相似性。此外，AntiFold 在以零样本方式预测抗体-抗原结合亲和力时实现了更强的相关性，而当纳入抗原信息时性能进一步增强。AntiFold 对破坏抗原结合的突变赋予低概率，与蛋白质语言模型的残基概率产生协同作用，并展示了在保留结构相关性质的同时指导抗体优化的前景。AntiFold 在 BSD 3-Clause 许可下免费提供，可通过网络服务器 https://opig.stats.ox.ac.uk/webapps/antifold/ 访问，并可通过 https://github.com/oxpig/AntiFold 以 pip 安装包形式获取。
英文摘要：
The design and optimization of antibodies requires an intricate balance across multiple properties. Protein inverse folding models, capable of generating diverse sequences folding into the same structure, are promising tools for maintaining structural integrity during antibody design. Here, we present AntiFold, an antibody-specific inverse folding model, fine-tuned from ESM-IF1 on solved and predicted antibody structures. AntiFold outperforms existing inverse folding tools on sequence recovery across complementarity-determining regions, with designed sequences showing high structural similarity to their solved counterpart. It additionally achieves stronger correlations when predicting antibody-antigen binding affinity in a zero-shot manner, while performance is augmented further when including antigen information. AntiFold assigns low probabilities to mutations that disrupt antigen binding, synergizing with protein language model residue probabilities, and demonstrates promise for guiding antibody optimization while retaining structure-related properties. AntiFold is freely available under the BSD 3-Clause as a web server at https://opig.stats.ox.ac.uk/webapps/antifold/ and and pip installable package at https://github.com/oxpig/AntiFold
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
