STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset...

Area of Science:

• Computational biology
• Structural bioinformatics
• Immunoinformatics

Background:

• Current computational methods for T-cell receptor (TCR) drug discovery and repertoire informatics do not fully leverage available structural data.
• There is a need for efficient tools to handle and analyze complex TCR structural information.

Purpose of the Study:

• To introduce STCRpy, an open-source Python package designed for high-throughput T-cell receptor (TCR) structure handling and analysis.
• To facilitate advanced computational analyses crucial for TCR drug discovery and repertoire informatics.

Main Methods:

• Development of an open-source Python package (STCRpy).
• Implementation of functions for high-throughput TCR structure processing and analysis.
• Integration of capabilities for TCR:peptide-MHC complex orientation calculation and scoring.
• Inclusion of tools for root-mean-square-distance evaluation and interaction profiling.

Main Results:

• STCRpy enables streamlined utilization of solved and predicted TCR structure data.
• The package facilitates complex analyses including orientation scoring and interaction profiling.
• STCRpy supports the curation of datasets for machine learning in TCR research.

Conclusions:

• STCRpy significantly enhances the utility of structural data in early-stage TCR drug discovery.
• The package provides a robust platform for advanced TCR repertoire informatics.
• STCRpy is freely available, promoting wider adoption and advancement in the field.