PyPropel: a Python-based tool for efficiently processing and characterising protein data

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 70 - 9
• Main Authors: Sun, Jianfeng, Ru, Jinlong, Cribbs, Adam P., Xiong, Dapeng
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.03.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Amino acid sequence; Amino acids; Analysis; Annotations; Binding sites; Bioinformatics; Biomedical and Life Sciences; Computational biology; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data pre-processing; Databases, Protein; Datasets; Life Sciences; Machine Learning; Metagenomics; Methods; Microarrays; Performance evaluation; Programming Languages; Protein features; Proteins; Proteins - chemistry; Quality control; Sequence analysis; Sequence Analysis, Protein - methods; Software; Solvents; Structural bioinformatics; Workflow; United States; Structural bioinformatics; Protein features; Data pre-processing; Sequence analysis; Machine learning
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06079-3

Background The volume of protein sequence data has grown exponentially in recent years, driven by advancements in metagenomics. Despite this, a substantial proportion of these sequences remain poorly annotated, underscoring the need for robust bioinformatics tools to facilitate efficient characterisation and annotation for functional studies. Results We present PyPropel, a Python-based computational tool developed to streamline the large-scale analysis of protein data, with a particular focus on applications in machine learning. PyPropel integrates sequence and structural data pre-processing, feature generation, and post-processing for model performance evaluation and visualisation, offering a comprehensive solution for handling complex protein datasets. Conclusion PyPropel provides added value over existing tools by offering a unified workflow that encompasses the full spectrum of protein research, from raw data pre-processing to functional annotation and model performance analysis, thereby supporting efficient protein function studies.