Predicting Peptide-MHC Binding Affinity With Imputed Training Data and Recurrent Neural Networks

Predicting the binding affinity between peptides (short amino acid sequences) and MHC proteins has emerged as a central problem in computational immunology due to its importance in determining the targets of T-cell mediated immune activity. An individual’s poly-clonal collection of T-cells is able to kill infected and cancerous cells while protecting healthy ones. This amazing feat is achieved through the winnowing and expansion of T-cell sub-populations possessing highly specific T-cell receptors (TCRs) (Blackman 1990). A distinct T-cell receptor recognizes a small number of similar peptides bound to an MHC molecule on the surface of a cell (Huseby 2005). Peptide-MHC binding is one of the most restrictive steps in “antigen processing” (Cresswell 2005) and is thus essential for determining which amino acid sequences can potentially trigger various T-cell responses.

Early approaches to peptide-MHC binding prediction focused on “sequence motifs”(Sette 1989), followed by regularized linear models, linear models with interaction terms such as SMM with pairwise features (Peters 2003). More recently, methods based on ensembles of shallow neural networks (Lundegaard 2008, Nielsen 2007) have become common tools in computational virology (Lund 2011), tumor immunology (Gubin 2015), and autoimmunity (Abreu 2012). Existing predictors work by encoding amino acid sequences as fixed length vectors using predefined amino acid features. In this poster we delineate several flavors of the peptide-MHC binding problem (i.e. allele-specific vs. pan-allele) and present the following improvements over the current generation of peptide-MHC binding predictors:

  • Learning vector embeddings for amino acids as part of training instead of using predefined features.

  • Generating synthetic data using imputation to train models for alleles with few training samples.

  • Replacing fixed length vector encodings with recurrent neural networks to make better predictions across a broader range of sequence lengths.

References

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