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

Journal article

Summary: Computational methods to guide early-stage TCR drug discovery and TCR repertoire informatics currently under-utilize solved and predicted structure data. Here, we streamline use of these data through an open-source python package for high-throughput TCR structure handling and analysis (STCRpy), facilitating analyses such as TCR:peptide-MHC complex orientation calculation/scoring, root-mean-square-distance evaluation, interaction profiling, and machine learning dataset curation. Availability and implementation: Freely available as a Python package at https://github.com/oxpig/STCRpy.

Authors: Quast, NP; Deane, CM; Raybould, MIJ

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Bioinformatics
• Volume: 41
• Issue: 10
• Pages: btaf566
• Article number: btaf566
• Publication date: 2025-10-10
• Acceptance date: 2025-10-06
• DOI: 10.1093/bioinformatics/btaf566
• EISSN: 1367-4811
• ISSN: 1367-4803
• Language: English