Identification of MHC Class II Binders/ Non-binders using Negative Selection Algorithm

The identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine design. These MHC class–II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptides is very helpful towards epitope-based vaccine design. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class–II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class–II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, and 0.69, and 0.84 respectively indicating good predictive performance for the small training set

MHCherryPan, a novel model to predict the binding affinity of pan-specific class I HLA-peptide

The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens, which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist. Due to the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this thesis, we presented a new algorithm to combine convolutional neural network and long short-term memory to solve this problem. Compared with other current popular algorithms, our model achieved the state-of-the-art results

Novel epitope based peptides for vaccine against SARS-CoV-2 virus: immunoinformatics with docking approach

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative viral strain for the contagious pandemic respiratory illness in humans which is a public health emergency of international concern. There is a desperate need for vaccines and antiviral strategies to combat the rapid spread of SARS-CoV-2 infection.Methods: The present study based on computational methods has identified novel conserved cytotoxic T-lymphocyte epitopes as well as linear and discontinuous B-cell epitopes on the SARS-CoV-2 spike (S) protein. The predicted MHC class I and class II binding peptides were further checked for their antigenic scores, allergenicity, toxicity, digesting enzymes and mutation.Results: A total of fourteen linear B-cell epitopes where GQSKRVDFC displayed the highest antigenicity-score and sixteen highly antigenic 100% conserved T-cell epitopes including the most potential vaccine candidates MHC class-I peptide KIADYNYKL and MHC class-II peptide VVFLHVTYV were identified. Furthermore, the potential peptide QGFSALEPL with high antigenicity score attached to larger number of human leukocyte antigen alleles. Docking analyses of the allele HLA-B*5201 predicted to be immunogenic to several of the selected epitopes revealed that the peptides engaged in strong binding with the HLA-B*5201 allele.Conclusions: Collectively, this research provides novel candidates for epitope-based peptide vaccine design against SARS-CoV-2 infection

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

BACKGROUND: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. RESULTS: The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 A(ROC )for the MHCBench data sets (up from 0.756), and an average of 0.96 A(ROC )for multiple alleles of the MHCPEP database. CONCLUSION: The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results: A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations

Topics in cancer genomics

Large-scale projects such as the The Cancer Genome Atlas (TCGA) have generated extensive exome libraries across several disease types and populations. Detection of somatic changes in HLA genes by whole-exome sequencing (WES) has been complicated by the highly polymorphic nature of these loci. We developed a method POLYSOLVER (POLYmorphic loci reSOLVER) for accurate inference of class I HLA-A, -B and -C alleles from WES data, and achieved 97% accuracy at protein level resolution when this was applied to 133 HapMap samples of known HLA type. By applying POLYSOLVER in conjunction with somatic change detection tools to 2688 tumor/normal pairs TCGA that were previously analyzed by conventional approaches, we re-discovered 37 of 56 (66%) HLA mutations, while further identifying 23 new events. An analysis of WES data from a larger set of 3768 tumor/normal pairs by POLYSOLVER revealed 131 class I mutations with an enrichment for potentially loss-of-function events. 3% of samples had at least one HLA event with 95 of 131 mutations in the T cell interacting and peptide binding domains. Recurrent hotspot sites of missense, nonsense and splice site mutations were discovered that suggest positive selection, and support immune evasion as an important pathway in cancer. Exome sequencing has also revealed a large number of shared and personal somatic mutations across human cancers. In principle, any genetic alteration affecting a protein-coding region has the potential to generate mutated peptides that are presented by surface HLA class I proteins that might be recognized by cytotoxic T cells. Utilizing POLYSOLVER in conjunction with knowledge of mutations in other genetic loci inferred from exome data, we developed a pipeline for the prediction and validation of such neoantigens derived from individual tumors and presented by patient-specific alleles of the HLA proteins. We applied our computational pipeline to 91 chronic lymphocytic leukemias (CLL) that had undergone whole-exome sequencing. We predicted ~22 mutated HLA-binding peptides per leukemia (derived from ~16 missense mutations), and experimentally confirmed HLA binding for ~55% of such peptides. Finally, we computationally predicted HLA-binding peptides with missense or frameshift mutations for several cancer types and predicted dozens to thousands of neoantigens per individual tumor, suggesting that neoantigens are frequent in most tumors. The neoantigen prediction pipeline can also elucidate the neoantigens unique to a particular cancer patient and help in the design of personalized immune vaccines. MicroRNAs (miRs) are a class of non-coding small RNAs that regulate gene expression by promoting mRNA degradation or by inhibiting mRNA translation. Context Likelihood of Relatedness (CLR) is genetic network reconstruction method that considers the local network context in assessing the significance of connections while also allowing for detection of non-linear associations. Leveraging TCGA multidimensional data in glioblastoma, we inferred the putative regulatory network between microRNA and mRNA using the CLR algorithm. Interrogation of the network in context of defined molecular subtypes identified 8 microRNAs with a strong discriminatory potential between proneural and mesenchymal subtypes. Integrative in silico analyses, a functional genetic screen, and experimental validation identified miR-34a as a tumor suppressor in proneural subtype glioblastoma. Mechanistically, in addition to its direct regulation of platelet-derived growth factor receptor-alpha (PDGFRA), promoter enrichment analysis of CLR-inferred mRNA nodes established miR-34a as a novel regulator of a SMAD4 transcriptional network. Clinically, miR-34a expression level is shown to be prognostic, where miR-34a low-expressing glioblastomas exhibited better overall survival. This work illustrates the potential of comprehensive multidimensional cancer genomic data combined with computational and experimental models to enable mechanistic exploration of relationships among different genetic elements across the genome space in cancer

Deciphering the Landscape of HLA class-I and class-II Phosphopeptidomes leads to Robust Predictions of Phosphorylated HLA ligands

Activation of CD8+ and CD4+ T cells through recognition of antigens presented by class I and class II human leukocyte antigen (HLA-I/HLA-II) molecules is crucial for immune responses against infected or malignant cells. In cancer, neoantigens can arise from cancer-specific genomic or proteomic alterations, including mutations and aberrant post-translational modification, such as phosphorylation. Identifying HLA ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. While much work has been done on unmodified HLA-I and HLA-II ligands, only little is known about the presentation of phosphorylated peptides, in particular by HLA-II molecules. Moreover, none of the existing in silico models for predictions of HLA – ligand interactions are specifically trained on phosphorylated ligands. This thesis presents in-depth analyses of phosphorylated HLA-I and HLA-II ligands and introduces predictors for HLA – phosphorylated ligand interactions. The first part of this thesis comprises the curation of phosphorylated HLA-I ligands from several Mass Spectrometry – based peptidomics studies, identifying more than 2,000 unique phosphorylated peptides covering 72 HLA-I alleles. Furthermore, it was see that phosphorylated HLA-I ligands are shaped by a combination of HLA-I binding motifs, intrinsic HLA-I binding properties of phosphorylated ligands and kinase motifs. Combining phosphorylated HLA-I ligands with unmodified data for training a prediction model resulted in improved predictions of phosphorylated HLA-I ligands. The second part addresses phosphorylated HLA-II ligands presented by professional antigen presenting cells for CD4+ T cell activation. MS – based HLA-II peptidomics data resulted in the identification of binding motifs for more than 30 HLA-II alleles, comprising 2,473 unique phosphorylated ligands. These were used to retrain a predictor for HLA-II - ligand interactions and showed improved accuracy for phosphorylated ligands. The analysis of the phosphorylated HLA-II peptidomes revealed a more diverse repertoire of kinases responsible for the phosphorylation of peptides presented on HLA-II compared to HLA-I. In summary, the current work presents in-depth studies on phosphorylated HLA ligands as well as bioinformatics tools for the predictions of phosphorylated peptide interactions with HLA-I and HLA-II molecules. -- L'activation des cellules T CD8+ et CD4+ suite à la reconnaissance d’antigènes présentés par les antigènes des leucocytes humains de classe I et II (HLA-I/HLA-II) est cruciale pour les réponses immunitaires contre les cellules infectées ou cancéreuses. Dans le cancer, les néoantigènes peuvent provenir d'altérations génomiques ou protéomiques spécifiques au cancer, par exemple des mutations ou des modifications post-traductionnelles aberrantes, telles que la phosphorylation. L'identification des ligands HLA reste une tâche difficile qui nécessite soit un travail expérimental lourd pour l'identification in vivo, soit des outils bio-informatiques optimisés pour des prédictions précises. Si beaucoup de travail a été réalisé sur les ligands HLA-I et HLA-II non modifiés, on ne sait que peu de choses sur la présentation des peptides phosphorylés, en particulier par les molécules HLA-II. De plus, aucun des modèles in silico existants pour la prédiction des interactions HLA - ligands n'est spécifiquement entraîné sur les ligands phosphorylés. Cette thèse présente des analyses détaillées sur les ligands HLA-I et HLA-II phosphorylés et introduit des prédicteurs pour les interactions HLA - ligands phosphorylés. La première partie de cette thèse comprend la curation des ligands HLA-I phosphorylés provenant de plusieurs études peptidiques de spectrométrie de masse, identifiant plus de 2’000 peptides phosphorylés uniques couvrant 72 allèles HLA-I. De plus, il a été constaté que les ligands HLA-I phosphorylés sont obtenus par une combinaison de motifs de liaison aux HLA-I, de propriétés intrinsèques de liaison entre les HLA-I et les ligands phosphorylés et de motifs de kinases. La combinaison de ces ligands HLA-I phosphorylés avec des données de ligands non modifiés pour l’entraînement du prédicteur a permis d'améliorer les prédictions des ligands HLA-I phosphorylés. La deuxième partie de cette thèse porte sur les ligands HLA-II phosphorylés qui sont présentés par des cellules présentatrices d'antigènes professionnelles pour l'activation des lymphocytes T CD4+. Les données peptidiques de HLA-II basées sur la spectrométrie de masse ont permis d'identifier des motifs de liaison pour plus de 30 allèles HLA-II, comprenant 2’473 ligands phosphorylés uniques. Ces motifs ont été utilisés pour re-entraîner un prédicteur des interactions entre les ligands et HLA-II qui a montré une meilleure précision pour les ligands phosphorylés. En outre, l'analyse du peptidome HLA-II phosphorylé a révélé un répertoire plus diversifié de kinases responsables de la phosphorylation des peptides présentés par les HLA-II par rapport aux HLA-I. En résumé, cette thèse présente des études détaillées sur les ligands HLA phosphorylés ainsi que des outils bio-informatiques pour la prédiction des interactions des peptides phosphorylés avec les molécules HLA-I et HLA-II