PELSA-Decipher: a software tool for the processing and interpretation of ligand-protein interaction datasets acquired by PELSA
Authors/Creators
Description
PELSA-Decipher is a software tool specifically designed to streamline the processing and analysis of PELSA data. It is an efficient and user-friendly software that offers the following key functionalities:
- Differential Analysis (DA): Quantitative comparison of differences between samples to facilitate the identification of target proteins.
- Protein Local Stability Analysis (ProLSA): Exploration of stability characteristics across different regions of a protein allowing the determination of binding regions.
- Concentration-Dependent Analysis (CDA): Analysis of the concentration-dependent curves at peptide level to enable the calculation of both local and global affinities of ligands for their target proteins.
The software supports importing domain information from the UniProt database, enabling precise analysis of ligand-protein binding regions. Users can also define custom regions for targeted analysis. In addition, it provides flexible image customization options, supports multiple output formats, and allows batch export of result reports and images.
Beyond PELSA-specific data, PELSA-Decipher is also compatible with certain other data types, further enhancing its versatility and functionality.
Developed in Python, the software’s graphical user interface (GUI) is built using PySide6. The tool is available as a packed .exe application, which can be directly downloaded and executed. Detailed instructions on installation, operation, troubleshooting, and additional resources are provided in the accompanying documentation.
Files
PELSA-Decipher_UserManual_1.6.0.0.pdf
Additional details
Software
- Repository URL
- https://github.com/DICP-1809/PELSA-Decipher
- Programming language
- Python
- Development Status
- Active
References
- PELSA-Decipher: A Software Tool for the Processing and Interpretation of Ligand–Protein Interaction Data Sets Acquired by PELSA, Haiyang Zhu, Keyun Wang, Kejia Li, Zheng Fang, Jiahua Zhou, Lianji Xue, Mingliang Ye, Journal of Proteome Research, 2025, 24 (9), 4623–4630..