pyCAST, a Python package for the detection of cavities on surface proteins

Identifying enzymatic activity sites, signal transduction pathways, and binding sites is crucial in biochemical research, prompting various methodologies. This study introduces pyCAST, a Python package designed to detect cavities on protein surfaces using the CAST methodology, a widely recognized approach for cavity identification. The paper describes the principle of the CAST methods and presents our implementation, including the results achieved over benchmark data sets. Furthermore, it discusses the limitations of the technique and potential future improvements. pyCAST is user-friendly, modular, adaptable to diverse applications, and openly available under the MIT license.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2025-01-01
DOI
https://doi.org/10.1016/j.csbj.2025.07.054
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

pyCAST, a Python package for the detection of cavities on surface proteins

Silvia Biasotti, Ulderico Fugacci, Giorgio Luciano
5 links
Computational and Structural Biotechnology Journal
Advanced Proteomics Techniques and Applications
article

pyCAST, a Python package for the detection of cavities on surface proteins

Silvia Biasotti, Ulderico Fugacci, Giorgio Luciano
article en

Abstract

Identifying enzymatic activity sites, signal transduction pathways, and binding sites is crucial in biochemical research, prompting various methodologies. This study introduces pyCAST, a Python package designed to detect cavities on protein surfaces using the CAST methodology, a widely recognized approach for cavity identification. The paper describes the principle of the CAST methods and presents our implementation, including the results achieved over benchmark data sets. Furthermore, it discusses the limitations of the technique and potential future improvements. pyCAST is user-friendly, modular, adaptable to diverse applications, and openly available under the MIT license.

Computational and Structural Biotechnology JournalVol. 27
Construction Technologies Institute (IT), National Research Council (IT)
European Commission
Openalex Percentile: Top 82%
Advanced Proteomics Techniques and Applications
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