STCRpy

STCRpy streamlines use of solved and predicted T-cell receptor structure data through an open-source Python package for high-throughput TCR structure handling and analysis to guide early-stage TCR drug discovery and TCR repertoire informatics.

Key Features:

• TCR:peptide-MHC complex orientation calculation and scoring: Calculates and scores orientations of TCR:peptide-MHC complexes.
• Root-mean-square-distance evaluation: Performs RMSD evaluation for structural comparison.
• Interaction profiling: Profiles interactions within TCR structures and complexes.
• Machine learning dataset curation: Curates datasets for machine learning applications.

Scientific Applications:

• TCR drug discovery: Guides early-stage TCR drug discovery through computational analysis of TCR structure data.
• TCR repertoire informatics: Supports TCR repertoire informatics by enabling high-throughput structural analysis.
• Machine learning in immunology: Prepares machine learning datasets from TCR structural data for predictive modeling.

Methodology:

Computational methods for parsing TCR structures, calculating TCR:peptide-MHC complex orientations, evaluating root-mean-square-distance, profiling interactions, and curating machine learning datasets.