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arXiv · 2402.17997

StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides

Zhe Wang·Jianping Wu·Mengjun Zheng·Chenchen Geng·Borui Zhen·Wei Zhang·Hui Wu·Zhengyang Xu·Gang Xu·Si Chen·Xiang Li
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Abstract

Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking.Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides.The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8).Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects.A machine learning-based predictor utilizing above calculated features was developed with AUC of 0.85, for identifying cell-penetrating hydrocarbon-stapled peptides.StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides.The source codes and dataset are freely available on Github: https://github.com/dahuilangda/stapep_package.

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Zhe Wang, Jianping Wu, Mengjun Zheng, Chenchen Geng, Borui Zhen, Wei Zhang, Hui Wu, Zhengyang Xu, Gang Xu, Si Chen, Xiang Li. 2024-02-28. StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides. https://arxiv.org/abs/2402.17997

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