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MHCflurry: open-source class I MHC binding affinity prediction

View ORCID ProfileTimothy O’Donnell, View ORCID ProfileAlex Rubinsteyn, Maria Bonsack, Angelika Riemer, View ORCID ProfileJeff Hammerbacher
doi: https://doi.org/10.1101/174243
Timothy O’Donnell
1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Alex Rubinsteyn
2Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Maria Bonsack
2Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
3Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, Germany
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Angelika Riemer
2Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
3Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, Germany
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Jeff Hammerbacher
1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
4Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, South Carolina, USA
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Abstract

Machine learning prediction of the interaction between major histocompatibility complex I (MHC I) proteins and their small peptide ligands is important for vaccine design and other applications in adaptive immunity. We describe and benchmark a new open-source MHC I binding prediction package, MHCflurry. The software is a collection of allele-specific binding predictors incorporating a novel neural network architecture and adhering to software development best practices. MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 on a benchmark of mass spec-identified MHC ligands and showed competitive accuracy on a benchmark of affinity measurements. The accuracy improvement was due to substantially better prediction of non-9-mer peptide ligands, which offset a narrowly lower accuracy on 9-mers. MHCflurry was on average 8.6X faster than NetMHC and 44X faster than NetMHCpan; performance is further increased when a graphics processing unit (GPU) is available. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, and may be installed using standard package managers.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 09, 2017.
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MHCflurry: open-source class I MHC binding affinity prediction
Timothy O’Donnell, Alex Rubinsteyn, Maria Bonsack, Angelika Riemer, Jeff Hammerbacher
bioRxiv 174243; doi: https://doi.org/10.1101/174243
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MHCflurry: open-source class I MHC binding affinity prediction
Timothy O’Donnell, Alex Rubinsteyn, Maria Bonsack, Angelika Riemer, Jeff Hammerbacher
bioRxiv 174243; doi: https://doi.org/10.1101/174243

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