StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides
arxiv(2024)
摘要
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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