Abstract
MHCflurry is an open source package for peptide/MHC I binding affinity prediction. Its command-line and programmatic interfaces make it well-suited for integration into high-throughput bioinformatic pipelines. Users can download models fit to publicly available data or train predictors on their own affinity measurements or mass spec datasets. This chapter gives a tutorial on essential MHCflurry functionality, including generating predictions, training new models, and using the MHCflurry Python interface. MHCflurry is available at https://github.com/openvax/mhcflurry.
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References
O’Donnell TJ, Rubinsteyn A, Bonsack M et al (2018) MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst 7:129–132.e4
Jurtz V, Paul S, Andreatta M et al (2017) NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol 199:3360–3368
Chollet F, Others (2015), Keras. https://github.com/keras-team/keras
Abadi M, Agarwal A, Barham P, et al (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems. http://arxiv.org/abs/1603.04467
Al-Rfou R, Alain G, Almahairi A, et al (2016) Theano: a Python framework for fast computation of mathematical expressions
Marsh SGE, Albert ED, Bodmer WF et al (2010) Nomenclature for factors of the HLA system, 2010. Tissue Antigens 75:291–455
Creech AL, Ting YS, Goulding SP et al (2018) The role of mass spectrometry and proteogenomics in the advancement of HLA epitope prediction. Proteomics 1700259:1–10
Vita R, Mahajan S, Overton JA et al (2019) The immune epitope database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343
Kim Y, Sidney J, Buus S et al (2014) Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions. BMC Bioinformatics 15:241
Shao W, Pedrioli PGA, Wolski W et al (2018) The SysteMHC atlas project. Nucleic Acids Res 46:D1237–D1247
Abelin JG, Keskin DB, Sarkizova S et al (2017) Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46:315–326
Sette A, Vitiello A, Reherman B et al (1994) The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol 153:5586–5592
Paul S, Weiskopf D, Angelo MA et al (2013) HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J Immunol 191:5831–5839
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift
Srivastava N, Hinton GE, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958
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O’Donnell, T., Rubinsteyn, A. (2020). High-Throughput MHC I Ligand Prediction Using MHCflurry. In: Boegel, S. (eds) Bioinformatics for Cancer Immunotherapy. Methods in Molecular Biology, vol 2120. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0327-7_8
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DOI: https://doi.org/10.1007/978-1-0716-0327-7_8
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