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Combining deep learning with immunopeptidomics and peptide microarrays to model peptide x human leukocyte antigen class II (HLA-II) interactions

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El Abd,  Hesham
Emmy Noether Research Group Evolutionary Immunogenomics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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El Abd, H. (2019). Combining deep learning with immunopeptidomics and peptide microarrays to model peptide x human leukocyte antigen class II (HLA-II) interactions. Master Thesis, Christian-Albrechts-Universität, Kiel.


Cite as: http://hdl.handle.net/21.11116/0000-0005-09A8-4
Abstract
The human leukocyte antigen class II (HLA-II) genes encode for a group of surface proteins expressed predominantly on antigen-presenting cells (APCs) where they present antigenic peptides to naïve CD4+ T cells. Various alleles and haplotypes at the HLA-II gene loci are genetically associated with several diseases, such as chronic inflammatory bowel disease (IBD). Experimental determination of the interactions between HLA-II proteins and antigenic peptides is an extremely challenging, laborious and expensive task. Thus, computational tools that can efficiently predict potentially binding peptides may help to reduce the peptide search space. Recently, deep learning (DL) has been proven to be a reliable solution to many challenging problems where big datasets are available. In my thesis, the utility of a novel high throughput microarray-based technology along with deep learning in modelling peptide HLA-II interactions have been evaluated. Data for six IBD-relevant HLA-DRB1 proteins have been included in the study. Coupling deep learning with microarray technology enabled the production of state-of-the-art models that can outcompete the current gold-standard tools for peptide-HLA-II interaction prediction. Furthermore, to increase the efficiency of deep learning model construction and development, the DeepImmuneTool (DIT) library which abstracts the process of building, training, and optimizing deep learning models has been developed. Concurrently to that, the Deep Antigen Screening (DAS) pipeline for large-scale peptide-HLA-II interaction prediction have been constructed and tested. Future work should aim at carrying more technical replicates to check for the reproducibility of the microarray technology, collecting more data about other HLA-II alleles and finally, fine-tune the developed software infrastructure to increase its efficiency and enhance its accessibility by other researchers.