Human Leukocyte Antigen Binding Research Articles

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Circular RNA as a source of neoantigens for cancer vaccines

BackgroundThe effectiveness of somatic neoantigen-based immunotherapy is often hindered by the limited number of mutations in tumors with low to moderate mutation burden. Focusing on microsatellite-stable colorectal cancer (CRC), this...

Simultaneous Thyroid Autoimmunity: A Coexistence of Grave's Disease and Hashimoto's Thyroiditis

Introduction: Grave’s Disease (GD) and Hashimoto's Thyroiditis (HT) are two autoimmune diseases whose coexistence is rare. We report a case of a patient in whom both diseases manifested simultaneously. Case report: A 36- year-old woman presented with thyrotoxicosis. She had tachycardia with WHO grade 2 homogeneously non-pulsatile goiter, without exophthalmos. The evaluation revealed peripheral hyperthyroidism with positive anti-thyroid peroxidase (anti-TPO) antibodies and anti-thyroid-stimulating hormone receptor (anti-TSHR) antibodies. Cervical ultrasound revealed a goiter affecting the right lobe in the context of thyroiditis. Thyroid scintigraphy indicated a picture suggestive of Graves' disease in the left lobe and hypocaptating thyroiditis in the right lobe. The patient was treated with propranolol and Carbimazole, with alternating phases of hyperthyroidism and hypothyroidism. Discussion and Conclusion: Autoimmune thyroid diseases, GD and HT, are specific to the thyroid gland and are common in women. They share pathogenic features that could explain their association, including genetic and environmental factors leading to thyroid cell damage, T-cell-mediated autoimmunity, human leukocyte antigen binding, and the presence of autoantibodies such as anti-thyroid peroxidase antibodies in GD. Rare cases have demonstrated that HT can manifest after GD, and vice versa, leading to an alternation of hyperthyroidism and hypothyroidism in certain patients due to changes in the balance between various categories of antibodies. In the present case, the patient simultaneously presented both diseases.

Systematic discovery of neoepitope–HLA pairs for neoantigens shared among patients and tumor types

The broad application of precision cancer immunotherapies is limited by the number of validated neoepitopes that are common among patients or tumor types. To expand the known repertoire of shared neoantigen–human leukocyte antigen (HLA) complexes, we developed a high-throughput platform that coupled an in vitro peptide–HLA binding assay with engineered cellular models expressing individual HLA alleles in combination with a concatenated transgene harboring 47 common cancer neoantigens. From more than 24,000 possible neoepitope–HLA combinations, biochemical and computational assessment yielded 844 unique candidates, of which 86 were verified after immunoprecipitation mass spectrometry analyses of engineered, monoallelic cell lines. To evaluate the potential for immunogenicity, we identified T cell receptors that recognized select neoepitope–HLA pairs and elicited a response after introduction into human T cells. These cellular systems and our data on therapeutically relevant neoepitopes in their HLA contexts will aid researchers studying antigen processing as well as neoepitope targeting therapies.

Association of Viral and Host Genetic Architecture with the Status of Neurocognitive Disorder in HIV-Infected Individuals.

The polymorphisms in host genes such as CCR5, CCR2, stromal derived factor (SDF), and MBL (mannose-binding lectin) as well as the viral nef gene have been shown to influence human immunodeficiency virus (HIV) infection, followed by the development of HIV-associated neurocognitive disorder (HAND). In this preliminary study with a limited number of samples, we have tried to associate the genetic polymorphism from the host and viral genetic factors with the neurocognitive status along with immuno-virological parameters. The total RNA was isolated from 10 unlinked plasma samples containing 5 samples from each group with and without HAND based on the International HIV Dementia Scale (IHDS) score <9.5 and >9.5, respectively. The CCR5, CCR2, SDF, MBL, and HIV nef genes were amplified and digested with restriction enzymes, except for the nef gene amplicon. Restrictions fragment length polymorphism (RFLP) was used to determine whether allelic variations were present in the digested host gene products, while sequencing was done for HIV nef amplicons without digestion. CCR5 delta 32 heterozygous variants were present in two samples from the HAND group. Three samples with HAND showed SDF-1 3' heterozygous allelic variant, while the MBL-2 gene presented with a homozygous mutant allele (D/D) in codon 52, heterozygous mutant allele (A/B) in codon 54, and codon 57 (A/C) for all samples except IHDS-2 irrespective of dementia status. Furthermore, amino acid alignment of Nef sequences confirmed the heterogeneity, while prediction of the human leukocyte antigen binding epitopes further explored its effect on functional motifs with variable binding efficiency such as epitopes GAFDLSFFL (aa 83) and LTFGWCFKL (aa 138) binding with HLA molecules at 60% and 80%, respectively. Thus, host genetics evidently influence predisposition to HIV infection and HAND. The genetic variability in the nef gene from both groups resulted in altering the functionality of specific domains and showing its impact on the progression of the disease, which needs to be explored.

IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity.

CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition's mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.

Abstract 3013: The immunogenetic basis of hepatitis B virus-related hepatocellular carcinoma

Abstract Background: The reported associations of genetic variants within the human leukocyte antigen (HLA) region with hepatis B virus (HBV)-related hepatocellular carcinoma (HCC) are not consistent, and the structure-function patterns involved in this disease remain unclear. Methods: Utilizing genotype data from 706 HBV-related HCC cases and 6197 chronic HBV carriers without HCC collected from three resources in Taiwan, we imputed the classical HLA alleles at 2-field resolution and analyzed the molecular landscape of HLA complexes by deeply dissecting their genotypic configurations, functional divergence, and HBV-antigen binding capabilities in associations with the HCC risk. Logistic regression models were used, and all models were adjusted for sex, age, and population structure using the top 10 principal components. We also tested the underlying biological implications of our findings by investigating the HLA binding affinities and HLA effect on control of viral replication (i.e, HBV DNA load), viral population diversity (i.e, quasispecies), and inflammation (i.e, chemokines, cytokines, and soluble programmed cell death 1 [sPD-1]-associated T cell exhaustion). Results: We found high accuracy (≥95% concordance) of HLA imputation at all HLA class I and class II alleles. The strongest evidence for an association with HCC was observed for HLA-DQB1*03:01 (adjusted OR= 1.35 in an additive genetic model, P=1.3×10-6). Three amino acids at specific positions within or near the peptide-binding groove of DQB1 molecules might explain the observed allele association, which leads to loss of binding HBV nucleocapsid protein (i.e., low binding affinity predicted by NetMHCIIpan 4.0 for HLA-DQB1*03:01). HLA-DQB1*03:01 was significantly associated with a higher HBV DNA load (P= 4.3×10-4) and an increased level of serum sPD-1 (P=6.8×10-4), but not with increased levels of chemokines, cytokines, and viral complexity (i.e., Shannon entropy for HBV quasispecies, all P values&amp;gt;0.05). Analysis of HLA zygosity confirmed that HLA-DQB1 heterozygosity was associated with a decreased risk of HCC (adjusted OR=0.77, P= 0.015). Analysis of HLA evolutionary divergence (HED) showed that high HED at HLA-DRB1 loci was marginally associated with a decreased risk of HCC among HLA-DQB1*03:01 non-carriers (P=0.05). Conclusion: Our data shed light on the immunogenetic risk associated with HBV-related HCC. The effect of HLA-DQB1*03:01 could be partly explained by its low binding affinity with HBV nucleocapsid antigen, and/or its effect on viral load control and sPD-1, but not by its control on other inflammation markers (chemokines and cytokines) and viral population diversity. The association between HLA-DQB1 heterozygosity and HCC risk could be independent of the HLA-DQB1*03:01 effect, which indicates underlying mechanisms related to increased immunosurveillance in clearing potential viral antigens or neoantigens. Citation Format: Zhiwei Liu, Chih-Jen Huang, Yu-Han Huang, Mei-Hung Pan, Mei-Hsuan Lee, Mathias Viard, Allan Hildesheim, Ruth M. Pfeiffer, Mary Carrington, Chien-Jen Chen, Tobias L. Lenz, Hwai-I Yang. The immunogenetic basis of hepatitis B virus-related hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3013.

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Schizophrenia, Human Leukocyte Antigen (HLA), and Herpes Viruses: Immunogenetic Associations at the Population Level

Several factors have been implicated in schizophrenia (SZ), including human herpes viruses (HHV) and the adaptive immunity Human Leukocyte Antigen (HLA) genes. Here we investigated these issues in 2 complementary ways. In one analysis, we evaluated SZ-HLA and HHV-HLA associations at the level of a single allele by computing (a) a SZ-HLA protection/susceptibility (P/S) score based on the covariance between SZ and 127 HLA allele prevalences in 14 European countries, (b) estimating in silico HHV-HLA best binding affinities for the 9 HHV strains, and (c) evaluating the dependence of P/S score on HHV-HLA binding affinities. These analyses yielded (a) a set of 127 SZ-HLA P/S scores, varying by >200× (maximum/minimum), which could not be accounted for by chance, (b) a set of 127 alleles × 9 HHV best-estimated affinities, varying by >600×, and (c) a set of correlations between SZ-HLA P/S scores and HHV-HLA binding which indicated a prominent role of HHV1. In a subsequent analysis, we extended these findings to the individual person by taking into account the fact that every individual carries 12 HLA alleles and computed (a) the average SZ-HLA P/S scores of 12 randomly chosen alleles (2 per gene), an indicator of HLA-based SZ P/S for an individual, and (b) the average of the corresponding HHV estimated affinities for those alleles, an indicator of overall effectiveness of HHV-HLA binding. We found (a) that HLA protection for SZ was significantly more prominent than susceptibility, and (b) that protective SZ-HLA scores were associated with higher HHV-HLA binding affinities, indicating that HLA binding and subsequent elimination of several HHV strains may confer protection against schizophrenia.

HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes.

Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.

Protective Association of HLA-DRB1*04 Subtypes in Neurodegenerative Diseases Implicates Acetylated Tau PHF6 Sequences

Objective To explore genetic association between human leukocyte antigen (HLA) and neurodegenerative diseases and investigate mechanisms behind the association. Background Pathophysiology of Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) involves accumulation of tau (neurofibrillary tangles) and amyloid-ß-rich (amyloid plaques) aggregates in AD, a-synuclein-rich aggregates (Lewy bodies) in PD and TDP-43 aggregates in ALS, although these aggregates may also co-occur. Likewise, consensus is growing that tau may play a key role in PD and ALS as well. Design/Methods We analyzed HLA associations in ∼176,000 individuals with PD or AD versus controls across ancestry groups. Pursuing this, we also compared postmortem brain density of neurofibrillary tangles and amyloid plaques in brain, tau and Aß42 levels in cerebrospinal fluid (CSF) of ∼8,000 individuals (controls and AD), and examined association of HLA in ∼2,500 patient with pathologically demonstrated Lewy Body Dementia. This was followed by HLA binding and tetramer T cell studies. Results A shared genetic association was observed across AD and PD at rs601945 (PD: odds ratio (OR) = 0.84; 95% confidence interval, [0.80; 0.88]; p = 2.2 x 10-13; AD: OR = 0.91[0.89; 0.93]; p = 1.8 x 10-22) and with a protective HLA association recently reported in ALS. Hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03 and absent for HLA-DRB1*04:05. The same signal was associated with decreased neurofibrillary tangle (but not neuritic plaque) density postmortem and was more associated with lower tau levels than Aß42 level changes in CSF. Furthermore, protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, but only when acetylated at K311, a modification central to aggregation. T cells recognizing this epitope were identified, showing relevance of this immune response in patients with neurodegenerative disorders. Conclusions An HLA-DRB1*04-mediated adaptive immune response, potentially against tau, decreases PD, AD and ALS risk, offering the possibility of new therapeutic avenues.

In silico Immunogenicity Assessment for Sequences Containing Unnatural Amino Acids: A Method Using Existing in silico Algorithm Infrastructure and a Vision for Future Enhancements.

The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and "correct" in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.

IEAtlas: an atlas of HLA-presented immune epitopes derived from non-coding regions.

Cancer-related epitopes can engage the immune system against tumor cells, thus exploring epitopes derived from non-coding regions is emerging as a fascinating field in cancer immunotherapies. Here, we described a database, IEAtlas (http://bio-bigdata.hrbmu.edu.cn/IEAtlas), which aims to provide and visualize the comprehensive atlas of human leukocyte antigen (HLA)-presented immunogenic epitopes derived from non-coding regions. IEAtlas reanalyzed publicly available mass spectrometry-based HLA immunopeptidome datasets against our integrated benchmarked non-canonical open reading frame information. The current IEAtlas identified 245 870 non-canonical epitopes binding to HLA-I/II allotypes across 15 cancer types and 30 non-cancerous tissues, greatly expanding the cancer immunopeptidome. IEAtlas further evaluates the immunogenicity via several commonly used immunogenic features, including HLA binding affinity, stability and T-cell receptor recognition. In addition, IEAtlas provides the biochemical properties of epitopes as well as the clinical relevance of corresponding genes across major cancer types and normal tissues. Several flexible tools were also developed to aid retrieval and to analyze the epitopes derived from non-coding regions. Overall, IEAtlas will serve as a valuable resource for investigating the immunogenic capacity of non-canonical epitopes and the potential as therapeutic cancer vaccines.

Enzymatic processing and MHC loading in cancer immunotherapyAdvanced immunopeptidomics based discovery engine for the development of personalized cancer immunotherapy

The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Mutated human leukocyte antigen binding peptides (HLAp) are currently the leading targets. Most studies attempt to identify neoantigens based on predicted affinity to HLA molecules. We have shown that the direct identification of tissue-derived neoantigens by mass spectrometry is becoming feasible and we have recently designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides that is suitable for both cell lines and tissues. The massive amount of HLAp data we acquire while hunting down the neo-antigens is highly valuable. We have compiled a large immunopeptidomics database across dozens of cell types and HLA allotypes. First, we have shown that by taking advantage of co-occurring HLA-I alleles across dozens of immunopeptidomics datasets we can rapidly and accurately identify HLA-I binding motifs. Consequently, training HLA-I ligand predictors on refined motifs significantly improves the identification of neoantigens. Recently, we have acquired the largest reported high-quality HLA-II peptidomics dataset. We introduced completely novel algorithmic tools to analyze such data and developed for the first time HLA-II epitope prediction tool trained on peptidomics data that results in major improvements in prediction accuracy. Second, our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features. We have shown as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, immunopeptidomics facilitates direct identification of neoantigens and it can also improve the prediction of clinically relevant neoantigens, and we develop a novel clinical pipeline for target selection for personalized anti-cancer vaccines.

How many post-translationally proteasomal spliced HLA-I peptides can be confidently identified in large scale MS/MS data?

Mass spectrometry-based immunopeptidomics is the method of choice for characterizing human leukocyte antigen binding peptides (HLAp) which play a key role in adaptive immunity. Recently two studies [1,2] reported that a large portion (20-40%) of HLAp’s originate from proteasomal splicing events. Both studies used large-scale mass spectrometry measurements of HLAp’s and bioinformatics analysis: the first study created a large sequence database containing all cis-spliced peptides to search the HLAp MS/MS spectra and the second study was based on de novo sequencing and subsequent filtration of cis-and trans-spliced peptides. We performed a reanalysis of the data of 1 and could not confirm most of the identifications reported in these studies 3,4 . Our results indicate that the proportion of HLAp that could be explained by proteasomal splicing was maximally a few percent. More recently, we reanalyzed part of the data from 2 . Comparison of predicted and observed hydrophobicity for the reported spliced and nonspliced peptides, and reanalysis of the data using state of the art MS/MS search engines indicate that the fraction of spliced peptides reported was vastly overestimated. Here we discuss the issues that arise when searching spliced peptides in large scale MS/MS data. We propose practical MS/MS search strategies and methods to properly control the error rate. We would like to emphasize that spliced peptides identifications from large-scale immunopeptidomics measurements should be carefully validated and confirmed with further biological experiments.

Binding Affinity Calculations of Gluten Peptides to HLA Risk Modifiers: DQ2.5 versus DQ7.5.

Free energy perturbation (FEP) calculations can predict relative binding affinities of an antigen and its point mutants to the same human leukocyte antigen (HLA) with high accuracy (e.g., within 1.0 kcal/mol to experiment); however, a more challenging task is to compare binding affinities of wholly different antigens binding to completely different HLAs using FEP. Researchers have used a variety of different FEP schemes to compute and compare absolute binding affinities, with varied success. Here, we propose and assess a unifying scheme to compute the relative binding affinities of different antigens binding to completely different HLAs using absolute binding affinity FEP calculations. We apply our affinity calculation technique to HLA-antigen-T-cell receptor (TCR) systems relevant to celiac disease (CeD) by investigating binding affinity differences between HLA-DQ2.5 (enhanced CeD risk) and HLA-DQ7.5 (CeD protective) in the binary (HLA-gliadin) and ternary (HLA-gliadin-TCR) binding complexes for three gliadin derived epitopes: glia-α1, glia-α2, and glia-ω1. Based on FEP calculations with our carefully designed thermodynamic cycles, we demonstrate that HLA-DQ2.5 has higher binding affinity than HLA-DQ7.5 for gliadin and enhanced binding affinity with a common TCR, agreeing with known results that the HLA-DQ2.5 serotype exhibits increased risk for CeD. Our findings reveal that our proposed absolute binding affinity FEP method is appropriate for predicting HLA binding for disparate antigens with different genotypes. We also discuss atomic-level details of HLA genotypes interacting with gluten peptides and TCRs in regard to the pathogenesis of CeD.

Human herpesvirus diversity is altered in HLA class I binding peptides

Herpesviruses are ubiquitous, genetically diverse DNA viruses, with long-term presence in humans associated with infrequent but significant pathology. Human leukocyte antigen (HLA) class I presents intracellularly derived peptide fragments from infected tissue cells to CD8+ T and natural killer cells, thereby directing antiviral immunity. Allotypes of highly polymorphic HLA class I are distinguished by their peptide binding repertoires. Because this HLA class I variation is a major determinant of herpesvirus disease, we examined if sequence diversity of virus proteins reflects evasion of HLA presentation. Using population genomic data from Epstein–Barr virus (EBV), human cytomegalovirus (HCMV), and Varicella–Zoster virus, we tested whether diversity differed between the regions of herpesvirus proteins that can be recognized, or not, by HLA class I. Herpesviruses exhibit lytic and latent infection stages, with the latter better enabling immune evasion. Whereas HLA binding peptides of lytic proteins are conserved, we found that EBV and HCMV proteins expressed during latency have increased peptide sequence diversity. Similarly, latent, but not lytic, herpesvirus proteins have greater population structure in HLA binding than nonbinding peptides. Finally, we found patterns consistent with EBV adaption to the local HLA environment, with less efficient recognition of EBV isolates by high-frequency HLA class I allotypes. Here, the frequency of CD8+ T cell epitopes inversely correlated with the frequency of HLA class I recognition. Previous analyses have shown that pathogen-mediated natural selection maintains exceptional polymorphism in HLA residues that determine peptide recognition. Here, we show that HLA class I peptide recognition impacts diversity of globally widespread pathogens.

Unsupervised Mining of HLA-I Peptidomes Reveals New Binding Motifs and Potential False Positives in the Community Database.

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design

Human leukocyte antigen (HLA) can recognize and bind foreign peptides to present them to specialized immune cells, then initiate an immune response. Computational prediction of the peptide and HLA (pHLA) binding can speed up immunogenic peptide screening and facilitate vaccine design. However, there is a lack of an automatic program to optimize mutated peptides with higher affinity to the target HLA allele. Here, to fill this gap, we develop the TransMut framework—composed of TransPHLA for pHLA binding prediction and an automatically optimized mutated peptides (AOMP) program—which can be generalized to any binding and mutation task of biomolecules. First, TransPHLA is developed by constructing a transformer-based model to predict pHLA binding, which is superior to 14 previous methods on pHLA binding prediction and neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed by exploiting the attention scores generated by TransPHLA to automatically optimize mutated peptides with higher affinity to the target HLA allele and with high homology to the source peptide. The proposed framework may automatically generate potential peptide vaccines for experimentalists.

Differential T cell immune responses to deamidated adeno-associated virus vector

Despite the high safety profile demonstrated in clinical trials, the immunogenicity of adeno-associated virus (AAV)-mediated gene therapy remains a major hurdle. Specifically, T-cell-mediated immune responses to AAV vectors are related to loss of efficacy and potential liver toxicities. As post-translational modifications in T cell epitopes have the potential to affect immune reactions, the cellular immune responses to peptides derived from spontaneously deamidated AAV were investigated. Here, we report that highly deamidated sites in AAV9 contain CD4 T cell epitopes with a Th1 cytokine pattern in multiple human donors with diverse human leukocyte antigen (HLA) backgrounds. Furthermore, some peripheral blood mononuclear cell (PBMC) samples demonstrated differential T cell activation to deamidated or non-deamidated epitopes. Also, in vitro and in silico HLA binding assays showed differential binding to the deamidated or non-deamidated peptides in some HLA alleles. This study provides critical attributes to vector-immune-mediated responses, as AAV deamidation can impact the immunogenicity, safety, and efficacy of AAV-mediated gene therapy in some patients.