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.
@article{arxiv.2402.17997,
title = {StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides},
author = {Zhe Wang and Jianping Wu and Mengjun Zheng and Chenchen Geng and Borui Zhen and Wei Zhang and Hui Wu and Zhengyang Xu and Gang Xu and Si Chen and Xiang Li},
journal= {arXiv preprint arXiv:2402.17997},
year = {2024}
}