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dc.contributor.authorAntunes, Dinler A.
Abella, Jayvee R.
Hall-Swan, Sarah
Devaurs, Didier
Conev, Anja
Moll, Mark
Lizée, Gregory
Kavraki, Lydia E.
dc.date.accessioned 2020-08-12T19:06:09Z
dc.date.available 2020-08-12T19:06:09Z
dc.date.issued 2020
dc.identifier.citation Antunes, Dinler A., Abella, Jayvee R., Hall-Swan, Sarah, et al.. "HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy." JCO Clinical Cancer Informatics, no. 4 (2020) ASCO: 623-636. https://doi.org/10.1200/CCI.19.00123.
dc.identifier.urihttps://hdl.handle.net/1911/109194
dc.description.abstract PURPOSE: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS: Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization. RESULTS: To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles. CONCLUSION: These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.
dc.language.iso eng
dc.publisher ASCO
dc.rightsLicensed under the Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy
dc.type Journal article
dc.citation.journalTitle JCO Clinical Cancer Informatics
dc.citation.issueNumber 4
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1200/CCI.19.00123
dc.type.publication publisher version
dc.citation.firstpage 623
dc.citation.lastpage 636


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