Wang Zhe, Wu Jianping, Zheng Mengjun, Geng Chenchen, Zhen Borui, Zhang Wei, Wu Hui, Xu Zhengyang, Xu Gang, Chen Si, Li Xiang
Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Hangzhou VicrobX Biotech Co., Ltd., Hangzhou 310018, China.
J Chem Inf Model. 2024 Dec 23;64(24):9361-9373. doi: 10.1021/acs.jcim.4c01718. Epub 2024 Nov 6.
All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against , with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.
全烃订书肽由于其共价侧链限制，具有更高的蛋白水解稳定性和膜通透性，使其优于线性肽。然而，目前缺乏用于提取结构和物理化学描述符以预测烃订书肽性质的工具。为了解决这一问题，我们推出了StaPep，这是一个基于Python的工具包，用于生成烃订书肽的三维结构并计算21种特征。StaPep支持含有两种非标准氨基酸（正亮氨酸和2-氨基异丁酸）和六种非天然锚定残基（S3、S5、S8、R3、R5和R8）的肽，并为其他非标准氨基酸提供定制选项。我们通过三个案例研究展示了StaPep的实用性。第一个案例生成了这些肽的三维结构，平均均方根偏差为1.62±0.86，为药物设计和生物活性预测提供了重要的结构见解。第二个案例基于计算出的分子特征开发了机器学习模型，以区分膜通透性和非通透性订书肽，曲线下面积达到0.93。第三个案例构建了回归模型，以预测订书肽对……的抗菌活性，皮尔逊相关系数为0.84。StaPep的流程涵盖了烃订书肽的数据检索、结构生成、特征计算和机器学习建模。源代码和数据集可在Github上免费获取：https://github.com/dahuilangda/stapep_package。
