[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125424-en":3,"doc-seo-125424-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125424,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Structural Bioinformatics - STCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation","Computational approaches for early-stage T-cell receptor (TCR) drug discovery and TCR repertoire informatics often underuse available solved and predicted structural data. STCRpy streamlines access through an open-source Python package for high-throughput TCR structure handling and analysis. It supports tasks including TCR:peptide-MHC orientation calculation/scoring, root-mean-square-distance evaluation, interaction profiling, and machine-learning dataset curation. The package is provided with Python API and command-line tools.","Structural Bioinformatics  \nSTCRpy: a software suite for T-cell receptor structure parsing, interaction profiling, and machine learning dataset preparation  \nNele P. Quast1 , Charlotte M. Deane􀀃 , 1 , Matthew I.J. Raybould􀀃 , 1   \n1 Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom  \n􀀃 Corresponding authors. Charlotte M. Deane, Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, United Kingdom. E-mail: [deane@stats.ox.ac.uk](deane@stats.ox.ac.uk); Matthew I.J. Raybould, Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, United [Kingdom. E-mail: matthew.raybould@stats.ox.ac.uk](Kingdom. E-mail: matthew.raybould@stats.ox.ac.uk).  \nAssociate Editor: Jianlin Cheng  \nAbstract  \nSummary: 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.  \nAvailability and implementation: Freely available as a Python package at [https://github.com/oxpig/STCRpy](https://github.com/oxpig/STCRpy).  \n1 Introduction  \nT cell receptors (TCRs) direct the adaptive immune response by interacting with antigens, such as short peptide fragments, presented in mammalian cells by the Major Histocompatibility Complex (pMHC) (van der Merwe and Dushek 2011, Birnbaum et al. 2014, Murphy and Casey 2017) . They are gaining increasing attention, as a basis for the antigen targeting arm of biotherapeutics, especially for cancer, whether in soluble or cellular modalities (Dolgin 2022, Shafer et al. 2022) .  \nThis coincides with the increased adoption of computational tools to streamline or reimagine the process of biotherapeutic molecule selection (Riley et al. 2016, Notin et al. 2024) . In the TCR field, this centers around unsupervised clustering or supervised prediction models that operate on the amino acid sequence and aim to assign the specificity of an expanded clone from a natural T-cell receptor repertoire (Hudson et al. 2023) .  \nThese methods have so far shown promise in the context of thoroughly studied antigens, but are unable to generalize to unseen antigen contexts or more complex determinants of binding (Meysman et al. 2022, Hudson et al. 2023) .  \nA strategy to improve the generalizability of models is to consider 3D structural information in addition to the sequence (Robinson et al. 2021) . While a few structure-aware methods exist (Bradley 2023, Ghoreyshi and George 2023, Wang et al. 2024), the overheads involved in accessing structural information and processing TCR:pMHC pose  \npredictions are a roadblock to new entrants to the field. While numerous immunoglobulin-specific software packages exist for sequence-based analysis of TCRs (e.g. Ye et al. 2013, Dunbar and Deane 2016), no such suite exists for annotating and processing TCR structure data, which are evermore abundant (Leem et al. 2018, Quast et al. 2025) .  \nOne bespoke processing step is calculating the relative orientation of the TCR to the pMHC, a property linked to the ability of cell-surface TCRs to trigger downstream signaling (Zareie et al. 2021) and to cross-reactivity and even autoreactivity (Beringer et al. 2015, Gras et al. 2016) . Many general protein-protein complex prediction software packages generate poses of signaling TCRs that fall outside the“canonical” angles consistent with this tenet of T cell biology. While methods have been proposed for calculating TCR geometries (Rudolph et al. 2006, Singh et al. 2020), there is no standard method with an accessible python interf","cbCaisYABMuZ1aFj","https://ap.wps.com/l/cbCaisYABMuZ1aFj","pdf",1418750,1,5,"English","en",105,"# Introduction\n## Limitations of sequence-only approaches\n## Motivation for structure-aware workflows\n## Need for standardized, reproducible tools\n# Implementation\n## Installation and interfaces\n## Parsing, annotation, and interaction analysis","[{\"question\":\"What problem does STCRpy address in TCR repertoire informatics?\",\"answer\":\"It streamlines the use of solved and predicted 3D structure data that are often underutilized in early-stage TCR analysis and drug discovery workflows.\"},{\"question\":\"What analyses can STCRpy perform for TCR:peptide-MHC complexes?\",\"answer\":\"STCRpy supports high-throughput structure parsing, orientation calculation/scoring, root-mean-square-distance evaluation, and interaction profiling across protein chains.\"},{\"question\":\"How does STCRpy enable machine learning dataset preparation?\",\"answer\":\"It curates machine learning-ready datasets from processed structural information, including interaction and geometric features derived from TCR:pMHC complexes.\"}]","Structural Bioinformatics - 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