Computational Medicine Program and Department of Pharmacology, University of North Carolina - Chapel Hill, Chapel Hill, North Carolina 27599, United States.
Curriculum in Bioinformatics and Computational Biology, University of North Carolina - Chapel Hill, Chapel Hill, North Carolina 27599, United States.
J Phys Chem B. 2024 Aug 1;128(30):7332-7340. doi: 10.1021/acs.jpcb.4c02047. Epub 2024 Jul 23.
Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.
预测蛋白质-肽相互作用对于理解肽结合过程和设计肽类药物至关重要。然而，由于肽的动力学缓慢和高灵活性，传统的计算建模方法在准确预测肽-蛋白结合结构方面面临挑战。在这里，我们引入了一种称为“PepBinding”的新工作流程，用于预测肽结合结构，该方法结合了肽对接、使用 Peptide Gaussian 加速分子动力学（Pep-GaMD）方法的全原子增强采样模拟以及结构聚类。PepBinding 已经在七个不同的模型肽上进行了验证。在使用 HPEPDOCK 进行肽对接时，相对于 X 射线结构，其结合构象的肽骨架均方根偏差（RMSD）范围为 3.8 至 16.0 Å，根据相互作用预测的关键评估（CAPRI）标准，这些模型的质量属于中等至不准确。仅进行 200 ns 的 Pep-GaMD 模拟就显著改善了对接模型，得到了五个中等质量和两个可接受质量的模型。因此，PepBinding 是一种用于预测肽结合结构的有效工作流程，可在 https://github.com/MiaoLab20/PepBinding 上公开获取。
