Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome


Identification of peptides presented by major histocompatibility complex class I (MHC-I) is important for multiple applications in immunology and cancer therapy. One especially promising area is neoantigen-based immunotherapy for cancer: for example, one patient with cholangiocarcinoma experienced a partial response lasting at least 2 years after infusion of tumor-infiltrating lymphocytes specific to a neoantigen in her tumor [1]. To select a suitable peptide target, investigators must identify immunogenic peptides that the patient’s MHC-I types are likely to present. Human leukocyte antigen (HLA) A, B, and C, the genes coding for MHC-I, are highly polymorphic, and each variant of MHC-I has a distinct preference for one or more binding motifs. Hence, the patient’s specific alleles determine the set of possible peptides presented. Further understanding is needed to identify which peptides MHC-I presents.

Mass spectrometry (MS) is one approach to determine peptide presentation by MHC-I. For example, MS can be used to sample the tumoral immunopeptidome after elution of MHC-peptide complexes. This method is highly accurate and thorough—indeed, it is the most reliable way to determine the peptides comprising the immunopeptidome. However, it is too costly and time-intensive for routine clinical use. Furthermore, it requires a relatively large amount of sample from the patient (up to 1cm3), which cannot always be obtained [2]. Computational methods, which are less costly and do not require samples, are thus valuable to predict which peptides a given allele of MHC-I will bind. Multiple predictors are publicly available to predict peptide presentation.

Multiple machine learning approaches have been taken toward predicting peptide presentation by MHC-I. Artificial neural networks (ANNs) are widely employed; they capture nonlinear information about the higher-order interactions among amino acids within the peptides [3]. A number of methods are based on ANNs, including NetMHC [4, 5]. NetMHC predicts the affinity of peptides for MHC-I alleles and is trained on chemical affinity data from in vitro assays. A related predictor, NetMHCstabpan, is trained on the half-life of the MHC-peptide complex in vitro [6]. Training data should represent the cases on which the predictor will be applied as much as possible, and the reliance of NetMHC and NetMHCstabpan on chemical affinity data limits their applicability to prediction of actual presentation in vivo by MHC-I. This is because peptide presentation is also contingent on other processes unrelated to chemical affinity. For example, proteasomal processing, abundance of proteins containing specific sequences, and biological half-life are important for peptide presentation but are not encoded within the affinity data used to train these predictors [7, 8]. Hence, NetMHC and NetMHCstabpan are suboptimal for extension to predicting peptide presentation in vivo because their training data are limited in biological relevance.

Mass spectrometry datasets are more suitable to train predictors of peptide binding: because these data describe epitopes actually presented in vivo, they encapsulate information about both chemical affinity and the aforementioned biological processes required for presentation. Furthermore, sampling the immunopeptidome does not require a priori peptide synthesis or selection, and this reduces the bias introduced by the investigator [7]. Immunopeptidomic surveying by mass spectrometry is thus more directly relevant than chemical affinities of investigator-selected peptides for predicting presence of a peptide in the immunopeptidome. Two other publicly available methods are trained on MS datasets: NetMHCpan and MixMHCpred. NetMHCpan is based on artificial neural networks—like others in the NetMHC family—and its training data include both measurements of chemical affinity and mass spectrometry elution data [9]. MixMHCpred is based on position weight matrices (PWMs) describing preferred peptide sequences established for each allele by a mixture model, and it is trained on mass spectrometry elution data alone [2]. The training data of these two predictors is highly biologically relevant, and they thus are more suitable for application to predicting presentation by MHC-I.

However, there remain unexplored applications of models to mass spectrometry datasets. A variety of features can be extracted from peptides, including categorical encodings of each residue, binary representations of certain functional groups, and continuous measurements of biophysical properties. There is an increasing reliance upon computationally complex deep neural networks in applications of machine learning to the biomedical sciences, yet these complex models do not always outperform simpler ones in highly diverse feature spaces [10]. Random forest models are well suited for classification in feature spaces including these different types of information. In prediction of peptide presentation, there is a heavy reliance upon artificial neural network models for classification and upon BLOSUM encoding as features [9]. Additional biochemical features and sequence representations have the potential to improve performance, and alternate machine learning frameworks such as random forests have the potential to outperform complex ANNs in these feature spaces. Herein, we use publicly available MS data to develop feature encodings and machine learning approaches toward optimizing prediction of peptide binding.

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