Predicting HLA class II antigen presentation through integrated deep learning

Abstract

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4 + T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.

Potential T-cell and B-cell Epitopes of 2019-nCoV

“…Complex (MHC or HLA) class I and class II is essential for anti-viral T-cell responses [14]. In contrast to B-cell epitopes, T-cell epitopes can be located anywhere in a viral protein since human cells can process and present both intracellular and extracellular viral peptides [15].…”

“…All protein fragments have the potential to be presented by MHC-I or MHC-II and recognized by T-cells. We apply NetMHCpan4 [17] and MARIA [15], two artificial neural network algorithms, to predict antigen presentation and identify potential T-cell epitopes.…”

DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

“…HLA genes are highly polymorphic, which allows them to fine-tune the adaptive immune system. Prediction of HLA-II binding peptides is important to vaccine design and targeted therapy development in immunology and cancer immunotherapy, but is challenging because HLA-II are highly polymorphic and the size of the peptides presented varies [1], [2]. As experimentally characterizing the binding specificity for all HLA molecules is costly in terms of time and labor, effective computational prediction methods are needed for HLA-II peptide binding affinity prediction.…”

“…Currently, NN-align(2009) [13], NetMHCIIpan-3.1(2015) [14], Comblib matrices(2008) [15], SMM-align(2007) [16], Tepitope(1999) [17] and Consensus IEDB (2008) [18] are included in this benchmark study, most of which were developed quite a while ago. More recently, there are several major reports on HLA-II peptide binding prediction [1], [2], [19]. First Garde et al [2] proposed to take advantage of a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen.…”

“…Their probablistic predictor of HLA-II ligands (MixMHC2pred) was shown to outperform the NetMHCIIPanII. At the same time, also in Nature Biotechnology, a long short-term memory (LSTM) based recurent neural network model,MARIA [1], was proposed to handle the high variability in the length of HLA-II peptide ligands (826 amino acids). When trained with both traditional HLA binding affinity data, the MS-based antigen presentation profiling datasets together with gene expression data and flanking residues of peptides, their deep learning model achieved significantly better prediction performance.…”