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.
Authors
- Silvia Biasotti (ORCID: https://orcid.org/0000-0002-9992-825X)
- Ulderico Fugacci (ORCID: https://orcid.org/0000-0003-3062-997X)
- Giorgio Luciano (ORCID: https://orcid.org/0000-0001-6353-8711)
Institutions
- Construction Technologies Institute (IT)
- National Research Council (IT)
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
Funders
- European Commission