基于DiffPepBuilder的靶标特异性从头肽结合物设计
Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
AI总结：
本文提出DiffPepBuilder，利用SE(3)-等变扩散模型和PepPC-F数据集从头协同设计靶标特异性肽序列与结构，并通过引入二硫键增强稳定性，在召回率、界面质量和多样性上优于现有方法。
摘要：
尽管靶标特异性从头蛋白质结合物设计取得了令人振奋的进展，但由于肽结构的柔性以及蛋白质-肽复合物结构数据的稀缺，肽结合物设计仍然具有挑战性。在本研究中，我们从丰富的蛋白质-蛋白质界面数据中整理了一个大型合成数据集，称为PepPC-F，并开发了DiffPepBuilder，这是一种从头靶标特异性肽结合物生成方法，利用在PepPC-F上训练的SE(3)-等变扩散模型来协同设计肽序列和结构。DiffPepBuilder还引入二硫键来稳定生成的肽结构。我们在30个具有可用蛋白质-肽复合物结构的实验验证的强肽结合物上测试了DiffPepBuilder。DiffPepBuilder能够有效召回肽配体的天然结构和序列，并生成具有改善的结合自由能的新型肽结合物。随后，我们对三个靶标进行了从头生成案例研究。在再生测试和案例研究中，DiffPepBuilder在序列和结构召回、界面质量和结构多样性方面均优于AfDesign以及与ProteinMPNN耦合的RFdiffusion。分子动力学模拟证实，二硫键的引入增强了生成肽的结构刚性和结合性能。作为一种通用的肽结合物从头设计工具，DiffPepBuilder可用于为具有三维和结合位点信息的给定蛋白质靶标设计肽结合物。
英文摘要：
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic dataset, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to co-design peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three dimensional and binding site information.
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。
