ArticleBioinformatics (Oxford, England)2025
STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Lymphodepleting chemotherapy potentiates neoantigen-directed T cell therapy by enhancing antigen presentation.Cell reports. Medicine · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
summaryComputational 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.