Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

neoANT-HILL: an integrated tool for identification of potential neoantigens

Background

Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.

Results

We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.

Conclusion

neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.

Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation

BACKGROUND

The novel coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the ongoing 2019-2020 pandemic. SARS-CoV-2 is a positive-sense single-stranded RNA coronavirus. Effective countermeasures against SARS-CoV-2 infection require the design and development of specific and effective vaccine candidates.

OBJECTIVE

To address the urgent need for a SARS-CoV-2 vaccine, in the present study, we designed and validated one cytotoxic T lymphocyte (CTL) and one helper T lymphocyte (HTL) multi-epitope vaccine (MEV) against SARS-CoV-2 using various in silico methods.

METHODS

Both designed MEVs are composed of CTL and HTL epitopes screened from 11 Open Reading Frame (ORF), structural and nonstructural proteins of the SARS-CoV-2 proteome. Both MEVs also carry potential B-cell linear and discontinuous epitopes as well as interferon gamma-inducing epitopes. To enhance the immune response of our vaccine design, truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1 was used as an adjuvant at the N termini of both MEVs. The tertiary models for both the designed MEVs were generated, refined, and further analyzed for stable molecular interaction with toll-like receptor 3. Codon-biased complementary DNA (cDNA) was generated for both MEVs and analyzed in silico for high level expression in a mammalian (human) host cell line.

RESULTS

In the present study, we screened and shortlisted 38 CTL, 33 HTL, and 12 B cell epitopes from the 11 ORF protein sequences of the SARS-CoV-2 proteome. Moreover, the molecular interactions of the screened epitopes with their respective human leukocyte antigen allele binders and the transporter associated with antigen processing (TAP) complex were positively validated. The shortlisted screened epitopes were utilized to design two novel MEVs against SARS-CoV-2. Further molecular models of both MEVs were prepared, and their stable molecular interactions with toll-like receptor 3 were positively validated. The codon-optimized cDNAs of both MEVs were also positively analyzed for high levels of overexpression in a human cell line.

CONCLUSIONS

The present study is highly significant in terms of the molecular design of prospective CTL and HTL vaccines against SARS-CoV-2 infection with potential to elicit cellular and humoral immune responses. The epitopes of the designed MEVs are predicted to cover the large human population worldwide (96.10%). Hence, both designed MEVs could be tried in vivo as potential vaccine candidates against SARS-CoV-2.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature

Peptide-based vaccine development needs accurate prediction of the binding affinity between major histocompatibility complex I (MHC I) proteins and their peptide ligands. Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools. However most of them are designed by the shallow neural networks. Bengio said that deep neural networks can learn better fits with less data than shallow neural networks. In our case, some of the alleles only have dozens of peptide data. In addition, we transform each peptide into a characteristic matrix and input it into the model. As we know when dealing with the problem that the input is a matrix, convolutional neural network (CNN) can find the most critical features by itself. Obviously, compared with the traditional neural network model, CNN is more suitable for predicting binding affinity. Different from the previous studies which are based on blocks substitution matrix (BLOSUM), we used novel feature to do the prediction. Since we consider that the order of the sequence, hydropathy index, polarity and the length of the peptide could affect the binding affinity and the properties of these amino acids are key factors for their binding to MHC, we extracted these information from each peptide. In order to make full use of the data we have obtained, we have integrated different lengths of peptides into 15mer based on the binding mode of peptide to MHC I. In order to demonstrate that our method is reliable to predict peptide-MHC binding, we compared our method with several popular methods. The experiments show the superiority of our method.

Proteome-wide screening for designing a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis using immunoinformatic approaches

Elizabethkingia anophelis is an emerging human pathogen causing neonatal meningitis, catheter-associated infections and nosocomial outbreaks with high mortality rates. Besides, they are resistant to most antibiotics used in empirical therapy. In this study, therefore, we used immunoinformatic approaches to design a prophylactic peptide vaccine against E. anophelis as an alternative preventive measure. Initially, cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes were predicted from the highest antigenic protein. The CTL and HTL epitopes together had a population coverage of 99.97% around the world. Eventually, six CTL, seven HTL, and two LBL epitopes were selected and used to construct a multi-epitope vaccine. The vaccine protein was found to be highly immunogenic, non-allergenic, and non-toxic. Codon adaptation and in silico cloning were performed to ensure better expression within E. coli K12 host system. The stability of the vaccine structure was also improved by disulphide bridging. In addition, molecular docking and dynamics simulation revealed strong and stable binding affinity between the vaccine and toll-like receptor 4 (TLR4) molecule. The immune simulation showed higher levels of T-cell and B-cell activities which was in coherence with actual immune response. Repeated exposure simulation resulted in higher clonal selection and faster antigen clearance. Nevertheless, experimental validation is required to ensure the immunogenic potency and safety of this vaccine to control E. anophelis infection in the future.Communicated by Ramaswamy H. Sarma.

Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors

In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.

Neoantigen prediction from genomic and transcriptomic data

Tumor cells acquire distinct genetic characteristics as a means to survive and proliferate indefinitely. Changes in the genetic code can also translate in changes at the protein level, therefore creating a distinguishable signature unique for tumor cells, and absent in normal tissues. The presence of discernable moieties in tumors is particularly attractive because it represents a therapeutic opportunity to target tumor cells with specificity, while sparing non-transformed cells. In this sense neoantigens, short peptides containing a mutated sequence, are seen attractive therapeutic targets because of their confinement within tumor cells. Neoantigens can be recognized with high affinity and specificity by tumor-targeting T cells, which consequently can initiate a potent anti-tumor immune response. While this is feasible and it has been tested in numerous cancer types including melanoma, colon and lung cancer, to mention a few, there are technical challenges in identifying immunogenic neoantigens. In this manuscript we address the topic of neoantigen identification from tumor samples, offering a technical overview of the bioinformatic methods utilized to profile the neoantigenic load of tumor samples obtained from clinical specimens. This is meant to guide readers through the steps of neoantigen identification using genomic data, by suggesting tools and methods that can provide, with a high degree of confidence, reliable results for downstream in vitro and in vivo applications.

Predicting HLA class II antigen presentation through integrated deep learning

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.

Antimicrobial Peptides and Vaccine Development to Control Multi-drug Resistant Bacteria

Antimicrobial resistance (AMR) reported to increase globally at alarming levels in the recent past. A number of potential alternative solutions discussed and implemented to control AMR in bacterial pathogens. Stringent control over the clinical application of antibiotics for a reduction in uses is a special consideration along with alternative solutions to fight against AMR. Although alternatives to conventional antibiotics like antimicrobial peptides (AMP) might warrant serious consideration to fight against AMR, there is a thriving recognition for vaccines in encountering the problem of AMR. Vaccines can reduce the prevalence of AMR by reducing the number of specific pathogens, which result in cutting down the antimicrobial need and uses. However, conventional vaccines produced using live or attenuated microorganisms while the presence of immunologically redundant biological components or impurities might cause major side effects and health related problems. Here we discussed AMPs based vaccination strategies as an emerging concept to overcome the disadvantages of traditional vaccines while boosting the AMPs to control multidrug resistant bacteria or AMR. Nevertheless, the poor immune response is a major challenge in the case of peptide vaccines as minimal antigenic epitopes used for immunization in peptide vaccines.

Antigen processing and presentation in HIV infection

The presentation of virus-derived peptides by MHC molecules constitutes the earliest signals for immune recognition by T cells. In HIV infection, immune responses elicited during infection do not enable to clear infection and correlates of immune protection are not well defined. Here we review features of antigen processing and presentation specific to HIV, analyze how HIV has adapted to the antigen processing machinery and discuss how advances in biochemical and computational protein degradation analyses and in immunopeptidome definition may help identify targets for efficient immune clearance and vaccine immunogen design.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Peptide-Based Subunit Vaccine Design of T- and B-Cells Multi-Epitopes against Zika Virus Using Immunoinformatics Approaches

The Zika virus disease, also known as Zika fever is an arboviral disease that became epidemic in the Pacific Islands and had spread to 18 territories of the Americas in 2016. Zika virus disease has been linked to several health problems such as microcephaly and the Guillain-Barré syndrome, but to date, there has been no vaccine available for Zika. Problems related to the development of a vaccine include the vaccination target, which covers pregnant women and children, and the antibody dependent enhancement (ADE), which can be caused by non-neutralizing antibodies. The peptide vaccine was chosen as a focus of this study as a safer platform to develop the Zika vaccine. In this study, a collection of Zika proteomes was used to find the best candidates for T- and B-cell epitopes using the immunoinformatics approach. The most promising T-cell epitopes were mapped using the selected human leukocyte antigen (HLA) alleles, and further molecular docking and dynamics studies showed a good peptide-HLA interaction for the best major histocompatibility complex-II (MHC-II) epitope. The most promising B-cell epitopes include four linear peptides predicted to be cross-reactive with T-cells, and conformational epitopes from two proteins accessible by antibodies in their native biological assembly. It is believed that the use of immunoinformatics methods is a promising strategy against the Zika viral infection in designing an efficacious multiepitope vaccine.

A common conserved peptide harboring predicted T and B cell epitopes in domain III of envelope protein of Japanese Encephalitis Virus and West Nile Virus for potential use in epitope based vaccines

Japanese encephalitis virus (JEV) and West Nile virus (WNV) are two major mosquito borne flaviviruses belonging to same serocomplex. JEV is transmitted by Culex mosquitoes and the reservoir host for the virus is pigs and/or water birds. WNV is also transmitted by Culex mosquitoes and reservoir host in this case is birds. It can also be transmitted through contact with other infected animals, their blood, or other tissues. The envelope protein of these viruses is the major source of epitopes and provides protective immunity. Bioinformatics tools were used to identify conserved epitopes in the envelope protein of these viruses. A conserved peptide "TPVGRLVTVNPFV" present in both the viruses containing predicted T and B cell epitopes was found. The model of one of the predicted epitope was generated and upon docking it bound in the groove of HLA-A0201 Class I MHC molecule. Further, it was amenable to proteasomal cleavage enhancing its chances of processing by cytosolic pathway. The peptide was found to be non toxic, non allergenic and stable in mammalian cells based on database search. The population coverage was pan world and nearly 70% identity of the peptide was found in the Zika virus envelope protein. The peptide was located in the domain III of envelope protein which is the exposed domain therefore B cell receptors may recognize this peptide easily. The conserved peptide containing T and B cell epitopes can have future application for designing epitope based vaccines for both JEV and WNV.

Immunogenicity of late stage specific peptide antigens of Mycobacterium tuberculosis

Global burden of latent TB infection comprises one-third of the world population. Identifying potential Mycobacterium tuberculosis (Mtb) latency associated antigens that can generate protective immunity against the pathogen is crucial for designing an effective TB vaccine. Usually the immune system responds to a small number of amino acids as MHC Class I or Class II peptides. The precision to trigger epitope specific protective T-cell immune response could therefore be achieved with synthetic peptide-based subunit vaccine. In the present study we have considered an immunoinformatic approach using available softwares (ProPred, IEDB, NETMHC, BIMAS, Vaxijen2.0) and docking and visualizing softwares (CABSDOCK, HEX, Pymol, Discovery Studio) to select 10 peptides as latency antigens from 4 proteins (Rv2626, Rv2627, Rv2628, and Rv2032) of DosR regulon of Mtb. As Intracellular IFN-γ secreted by T cells is the most essential cytokine in Th1 mediated protective immunity, these peptides were verified as potential immunogenic epitopes in Peripheral Blood Mononuclear Cells (PBMCs) of 10 healthy contacts of TB patients (HTB) and 10 Category I Pulmonary TB patients (PTB).The antigen-specific CD4 and CD8 T cells expressing intracellular IFN-γ were analyzed using monoclonal antibodies in all subjects by multi-parameter flow cytometry. Both, PTB and HTB individuals responded to DosR peptides by showing increased frequency of IFN-γ⁺CD4 and IFN-γ⁺CD8 T cells. The T-cell responses were significantly higher in PTB patients in comparision to the HTB individuals. Additionally, our synthetic peptides and pools showed higher frequencies of IFN-γ⁺CD4 and IFN-γ⁺CD8 T cells than the peptides of Ag85B. This pilot study can be taken up further in larger sample size which may support the untapped opportunity of designing Mtb DosR inclusive peptide based post-exposure subunit vaccine.

Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data

Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.

Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design

The computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in peptide-MHC affinity prediction that arises from observational noise and the lack of relevant training examples. With PUFFIN's uncertainty metrics, we define binding likelihood, the probability a peptide binds to a given MHC allele at a specified affinity threshold. Compared to affinity point estimates, we find that binding likelihood correlates better with the observed affinity and reduces false positives in high-affinity peptide design. When applied to examine an existing peptide vaccine, PUFFIN identifies an alternative vaccine formulation with higher binding likelihood. PUFFIN is freely available for download at http://github.com/gifford-lab/PUFFIN.

Exploratory Study of Predicted Indirectly ReCognizable HLA Epitopes in Mismatched Hematopoietic Cell Transplantations

HLA-mismatches in hematopoietic stem-cell transplantation are associated with an impaired overall survival (OS). The aim of this study is to explore whether the Predicted Indirectly ReCognizable HLA-Epitopes (PIRCHE) algorithm can be used to identify HLA-mismatches that are related to an impaired transplant outcome. PIRCHE are computationally predicted peptides derived from the patient's mismatched-HLA molecules that can be presented by donor-patient shared HLA. We retrospectively scored PIRCHE numbers either presented on HLA class-I (PIRCHE-I) or class-II (PIRCHE-II) for a Dutch multicenter cohort of 103 patients who received a single HLA-mismatched (9/10) unrelated donor transplant in an early phase of their disease. These patients were divided into low and high PIRCHE-I and PIRCHE-II groups, based on their PIRCHE scores, and compared using multivariate statistical analysis methods. The high PIRCHE-II group had a significantly impaired OS compared to the low PIRCHE-II group and the 10/10 reference group (HR: 1.86, 95%-CI: 1.02–3.40; and HR: 2.65, 95%-CI: 1.53–4.60, respectively). Overall, PIRCHE-II seem to have a more prominent effect on OS than PIRCHE-I. This impaired OS is probably due to an increased risk for severe acute graft-vs.-host disease. These data suggest that high PIRCHE-II scores may be used to identify non-permissible HLA mismatches within single HLA-mismatched hematopoietic stem-cell transplantations.

Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors

BACKGROUND

Recent genomic and bioinformatic technological advances have made it possible to dissect the immune response to personalized neoantigens encoded by tumor-specific mutations. However, timely and efficient identification of neoantigens is still one of the major obstacles to using personalized neoantigen-based cancer immunotherapy.

METHODS

Two different pipelines of neoantigens identification were established in this study: (1) Clinical grade targeted sequencing was performed in patients with refractory solid tumor, and mutant peptides with high variant allele frequency and predicted high HLA-binding affinity were de novo synthesized. (2) An inventory-shared neoantigen peptide library of common solid tumors was constructed, and patients' hotspot mutations were matched to the neoantigen peptide library. The candidate neoepitopes were identified by recalling memory T-cell responses in vitro. Subsequently, neoantigen-loaded dendritic cell vaccines and neoantigen-reactive T cells were generated for personalized immunotherapy in six patients.

RESULTS

Immunogenic neo-epitopes were recognized by autologous T cells in 3 of 4 patients who utilized the de novo synthesis mode and in 6 of 13 patients who performed shared neoantigen peptide library, respectively. A metastatic thymoma patient achieved a complete and durable response beyond 29 months after treatment. Immune-related partial response was observed in another patient with metastatic pancreatic cancer. The remaining four patients achieved the prolonged stabilization of disease with a median PFS of 8.6 months.

CONCLUSIONS

The current study provided feasible pipelines for neoantigen identification. Implementing these strategies to individually tailor neoantigens could facilitate the neoantigen-based translational immunotherapy research.TRIAL REGSITRATION. ChiCTR.org ChiCTR-OIC-16010092, ChiCTR-OIC-17011275, ChiCTR-OIC-17011913; ClinicalTrials.gov NCT03171220.

FUNDING

This work was funded by grants from the National Key Research and Development Program of China (Grant No. 2017YFC1308900), the National Major Projects for "Major New Drugs Innovation and Development" (Grant No.2018ZX09301048-003), the National Natural Science Foundation of China (Grant No. 81672367, 81572329, 81572601), and the Key Research and Development Program of Jiangsu Province (No. BE2017607).

T cell epitopes of Per a 10 modulate local-systemic immune responses and airway inflammation by augmenting Th1 and T regulatory cell functions in murine model

Peptide immunotherapy (PIT) represents a safe and efficacious therapeutic modality for allergic diseases. Present study evaluates immunotherapeutic potential of T cell peptides of major cockroach allergen, Per a 10 in murine model of airway allergy. Treatment with peptides T-P8 and T-P10 demonstrated maximal resolution of pathophysiological features such as reduced recruitment of inflammatory cells to airways, lowered specific IgE, induction of IgG2a antibodies in serum, immune deviation towards Th1 cytokine milieu, suppression of Th2 cytokines in BALF and splenocyte culture supernatant and resolution of lung inflammation. A significant increase in CD4⁺Foxp3⁺ cells in spleen indicate towards induction of T regulatory cell mediated peripheral tolerance characterized by shift in cytokine milieu from Th2 to T regulatory cytokines. PIT modulates regulation of immune responses at both local and systemic levels, contributes towards holistic improvement in allergic features in mice and thus demonstrate potential for safe, specific and efficacious treatment for cockroach allergy.

Design of novel multi-epitope vaccines against severe acute respiratory syndrome validated through multistage molecular interaction and dynamics

Severe acute respiratory syndrome (SARS) is endemic in South China and is continuing to spread worldwide since the 2003 outbreak, affecting human population of 37 countries till present. SARS is caused by the severe acute respiratory syndrome Coronavirus (SARS-CoV). In the present study, we have designed two multi-epitope vaccines (MEVs) composed of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B cell epitopes overlap, bearing the potential to elicit cellular as well as humoral immune response. We have used truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1 as molecular adjuvants at N-terminal of both the MEVs. Selected overlapping epitopes of both the MEVs were further validated for stable molecular interactions with their respective human leukocyte antigen class I and II allele binders. Moreover, CTL epitopes were further studied for their molecular interaction with transporter associated with antigen processing. Furthermore, after tertiary structure modelling, both the MEVs were validated for their stable molecular interaction with Toll-like receptors 2 and 4. Codon-optimized cDNA of both the MEVs was analysed for their potential high level of expression in the mammalian cell line (Human) needed for their further in vivo testing. Overall, the present study proposes in silico validated design of two MEVs against SARS composed of specific epitopes with the potential to cause a high level of SARS-CoV specific cellular as well as humoral immune response. Communicated by Ramaswamy H. Sarma.

APOBEC3G Regulation of the Evolutionary Race Between Adaptive Immunity and Viral Immune Escape Is Deeply Imprinted in the HIV Genome

APOBEC3G (A3G) is a host enzyme that mutates the genomes of retroviruses like HIV. Since A3G is expressed pre-infection, it has classically been considered an agent of innate immunity. We and others previously showed that the impact of A3G-induced mutations on the HIV genome extends to adaptive immunity also, by generating cytotoxic T cell (CTL) escape mutations. Accordingly, HIV genomic sequences encoding CTL epitopes often contain A3G-mutable “hotspot” sequence motifs, presumably to channel A3G action toward CTL escape. Here, we studied the depths and consequences of this apparent viral genome co-evolution with A3G. We identified all potential CTL epitopes in Gag, Pol, Env, and Nef restricted to several HLA class I alleles. We simulated A3G-induced mutations within CTL epitope-encoding sequences, and flanking regions. From the immune recognition perspective, we analyzed how A3G-driven mutations are predicted to impact CTL-epitope generation through modulating proteasomal processing and HLA class I binding. We found that A3G mutations were most often predicted to result in diminishing/abolishing HLA-binding affinity of peptide epitopes. From the viral genome evolution perspective, we evaluated enrichment of A3G hotspots at sequences encoding CTL epitopes and included control sequences in which the HIV genome was randomly shuffled. We found that sequences encoding immunogenic epitopes exhibited a selective enrichment of A3G hotspots, which were strongly biased to translate to non-synonymous amino acid substitutions. When superimposed on the known mutational gradient across the entire length of the HIV genome, we observed a gradient of A3G hotspot enrichment, and an HLA-specific pattern of the potential of A3G hotspots to lead to CTL escape mutations. These data illuminate the depths and extent of the co-evolution of the viral genome to subvert the host mutator A3G.

Construction and Screening of an Antigen-Derived Peptide Library Displayed on Yeast Cell Surface for CD4+ T Cell Epitope Identification

Antigenic peptides (termed T cell epitopes) are assembled with major histocompatibility complex (MHC) molecules and presented on the surface of antigen-presenting cells (APCs) for T cell recognition. T cells engage these peptide-MHCs using T cell receptors (TCRs). Because T cell epitopes determine the specificity of a T cell immune response, their prediction and identification are important steps in developing peptide-based vaccines and immunotherapies. In recent years, a number of computational methods have been developed to predict T cell epitopes by evaluating peptide-MHC binding; however, the success of these methods has been limited for MHC class II (MHCII) due to the structural complexity of MHCII antigen presentation. Moreover, while peptide-MHC binding is a prerequisite for a T cell epitope, it alone is not sufficient. Therefore, T cell epitope identification requires further functional verification of the MHC-binding peptide using professional APCs, which are difficult to isolate, expand, and maintain. To address these issues, we have developed a facile, accurate, and high-throughput method for T cell epitope mapping by screening antigen-derived peptide libraries in complex with MHC protein displayed on yeast cell surface. Here, we use hemagglutinin and influenza A virus X31/A/Aichi/68 as examples to describe the key steps in identification of CD4+ T cell epitopes from a single antigenic protein and the entire genome of a pathogen, respectively. Methods for single-chain peptide MHC vector design, yeast surface display, peptide library generation in Escherichia coli, and functional screening in Saccharomyces cerevisiae are discussed.

Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach

Background

Middle East respiratory syndrome (MERS) is caused by MERS coronavirus (MERS-CoV). Thus far, MERS outbreaks have been reported from Saudi Arabia (2013 and 2014) and South Korea (2015). No specific vaccine has yet been reported against MERS.

Purpose

To address the urgent need for an MERS vaccine, in the present study, we have designed two multi-epitope vaccines (MEVs) against MERS utilizing several in silico methods and tools.

Methods

The design of both the multi-epitope vaccines (MEVs) are composed of cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes, screened form thirteen different proteins of MERS-CoV. Both the MEVs also carry potential B-cell linear epitope regions, B-cell discontinuous epitopes as well as interferon-γ-inducing epitopes. Human β-defensin-2 and β-defensin-3 were used as adjuvants to enhance the immune response of MEVs. To design the MEVs, short peptide molecular linkers were utilized to link screened most potential CTL epitopes, HTL epitopes and the adjuvants. Tertiary models for both the MEVs were generated, refined, and further studied for their molecular interaction with toll-like receptor 3. The cDNAs of both MEVs were generated and analyzed in silico for their expression in a mammalian host cell line (human).

Results

Screened CTL and HTL epitopes were found to have high propensity for stable molecular interaction with HLA alleles molecules. CTL epitopes were also found to have favorable molecular interaction within the cavity of transporter associated with antigen processing. The selected CTL and HTL epitopes jointly cover upto 94.0% of worldwide human population. Both the CTL and HTL MEVs molecular models have shown to have stable binding and complex formation propensity with toll-like receptor 3. The cDNA analysis of both the MEVs have shown high expression tendency in mammalian host cell line (human).

Conclusion

After multistage in silico analysis, both the MEVs are predicted to elicit humoral as well as cell mediated immune response. Epitopes of the designed MEVs are predicted to cover large human population worldwide. Hence both the designed MEVs could be tried in vivo as potential vaccine candidates against MERS.

Footprints of antigen processing boost MHC class II natural ligand predictions

Background

Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.

Methods

We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets.

Results

We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand.

Conclusions

The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0594-6) contains supplementary material, which is available to authorized users.

A proteomics approach for the identification of species-specific immunogenic proteins in the Mycobacterium abscessus complex

The Mycobacterium abscessus complex can cause fatal pulmonary disease, especially in cystic fibrosis patients. Diagnosing M. abscessus complex pulmonary disease is challenging. Immunologic assays specific for M. abscessus are not available. In this study seven clinical M. abscessus complex strains and the M. abscessus reference strain ATCC19977 were used to find species-specific proteins for their use in immune assays. Six strains showed rough and smooth colony morphotypes simultaneously, two strains only showed rough mophotypes, resulting in 14 separate isolates. Clinical isolates were submitted to whole genome sequencing. Proteomic analysis was performed on bacterial lysates and culture supernatant of all 14 isolates. Species-specificity for M. abscessus complex was determined by a BLAST search for proteins present in all supernatants. Species-specific proteins underwent in silico B- and T-cell epitope prediction. All clinical strains were found to be M. abscessus ssp. abscessus. Mutations in MAB_4099c as a likely genetic basis of the rough morphotype were found in six out of seven clinical isolates. 79 proteins were present in every supernatant, of which 12 are exclusively encoded by all members of M. abscessus complex plus Mycobacterium immunogenum. In silico analyses predicted B- and T-cell epitopes in all of these 12 species-specific proteins.

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition

Accurate predictions of T-cell epitopes would be useful for designing vaccines, immunotherapies for cancer and autoimmune diseases, and improved protein therapies. The humoral immune response involves uptake of antigens by antigen presenting cells (APCs), APC processing and presentation of peptides on MHC class II (pMHCII), and T-cell receptor (TCR) recognition of pMHCII complexes. Most in silico methods predict only peptide-MHCII binding, resulting in significant over-prediction of CD4 T-cell epitopes. We present a method, ITCell, for prediction of T-cell epitopes within an input protein antigen sequence for given MHCII and TCR sequences. The method integrates information about three stages of the immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition. First, antigen cleavage sites are predicted based on the cleavage profiles of cathepsins S, B, and H. Second, for each 12-mer peptide in the antigen sequence we predict whether it will bind to a given MHCII, based on the scores of modeled peptide-MHCII complexes. Third, we predict whether or not any of the top scoring peptide-MHCII complexes can bind to a given TCR, based on the scores of modeled ternary peptide-MHCII-TCR complexes and the distribution of predicted cleavage sites. Our benchmarks consist of epitope predictions generated by this algorithm, checked against 20 peptide-MHCII-TCR crystal structures, as well as epitope predictions for four peptide-MHCII-TCR complexes with known epitopes and TCR sequences but without crystal structures. ITCell successfully identified the correct epitopes as one of the 20 top scoring peptides for 22 of 24 benchmark cases. To validate the method using a clinically relevant application, we utilized five factor VIII-specific TCR sequences from hemophilia A subjects who developed an immune response to factor VIII replacement therapy. The known HLA-DR1-restricted factor VIII epitope was among the six top-scoring factor VIII peptides predicted by ITCall to bind HLA-DR1 and all five TCRs. Our integrative approach is more accurate than current single-stage epitope prediction algorithms applied to the same benchmarks. It is freely available as a web server (http://salilab.org/itcell).

A comprehensive in silico analysis for identification of therapeutic epitopes in HPV16, 18, 31 and 45 oncoproteins

Human papillomaviruses (HPVs) are a group of circular double-stranded DNA viruses, showing severe tropism to mucosal tissues. A subset of HPVs, especially HPV16 and 18, are the primary etiological cause for several epithelial cell malignancies, causing about 5.2% of all cancers worldwide. Due to the high prevalence and mortality, HPV-associated cancers have remained as a significant health problem in human society, making an urgent need to develop an effective therapeutic vaccine against them. Achieving this goal is primarily dependent on the identification of efficient tumor-associated epitopes, inducing a robust cell-mediated immune response. Previous information has shown that E5, E6, and E7 early proteins are responsible for the induction and maintenance of HPV-associated cancers. Therefore, the prediction of major histocompatibility complex (MHC) class I T cell epitopes of HPV16, 18, 31 and 45 oncoproteins was targeted in this study. For this purpose, a two-step plan was designed to identify the most probable CD8+ T cell epitopes. In the first step, MHC-I and II binding, MHC-I processing, MHC-I population coverage and MHC-I immunogenicity prediction analyses, and in the second step, MHC-I and II protein-peptide docking, epitope conservation, and cross-reactivity with host antigens’ analyses were carried out successively by different tools. Finally, we introduced five probable CD8+ T cell epitopes for each oncoprotein of the HPV genotypes (60 epitopes in total), which obtained better scores by an integrated approach. These predicted epitopes are valuable candidates for in vitro or in vivo therapeutic vaccine studies against the HPV-associated cancers. Additionally, this two-step plan that each step includes several analyses to find appropriate epitopes provides a rational basis for DNA- or peptide-based vaccine development.

Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus

Oropouche virus (OROV) is an emerging pathogen which causes Oropouche fever and meningitis in humans. Several outbreaks of OROV in South America, especially in Brazil, have changed its status as an emerging disease, but no vaccine or specific drug target is available yet. Our approach was to identify the epitope-based vaccine candidates as well as the ligand-binding pockets through the use of immunoinformatics. In this report, we identified both T-cell and B-cell epitopes of the most antigenic OROV polyprotein with the potential to induce both humoral and cell-mediated immunity. Eighteen highly antigenic and immunogenic CD8⁺ T-cell epitopes were identified, including three 100% conserved epitopes (TSSWGCEEY, CSMCGLIHY, and LAIDTGCLY) as the potential vaccine candidates. The selected epitopes showed 95.77% coverage for the mixed Brazilian population. The docking simulation ensured the binding interaction with high affinity. A total of five highly conserved and nontoxic linear B-cell epitopes "NQKIDLSQL," "HPLSTSQIGDRC," "SHCNLEFTAITADKIMSL," "PEKIPAKEGWLTFSKEHTSSW," and "HHYKPTKNLPHVVPRYH" were selected as potential vaccine candidates. The predicted eight conformational B-cell epitopes represent the accessibility for the entered virus. In the posttherapeutic strategy, ten ligand-binding pockets were identified for effective inhibitor design against emerging OROV infection. Collectively, this research provides novel candidates for epitope-based peptide vaccine design against OROV.

MHC-associated peptide proteomics enabling highly sensitive detection of immunogenic sequences for the development of therapeutic antibodies with low immunogenicity

Immunogenicity is a key factor capable of influencing the efficacy and safety of therapeutic antibodies. A recently developed method called MHC-associated peptide proteomics (MAPPs) uses liquid chromatography/mass spectrometry to identify the peptide sequences derived from a therapeutic protein that are presented by major histocompatibility complex class II (MHC II) on antigen-presenting cells, and therefore may induce immunogenicity. In this study, we developed a MAPPs technique (called Ab-MAPPs) that has high throughput and can efficiently identify the MHC II-presented peptides derived from therapeutic antibodies using magnetic nanoparticle beads coated with a hydrophilic polymer in the immunoprecipitation process. The magnetic beads could identify more peptides and sequence regions originating from infliximab and adalimumab in a shorter measurement time than Sepharose beads, which are commonly used for MAPPs. Several sequence regions identified by Ab-MAPPs from infliximab corresponded to immunogenic sequences reported by other methods, which suggests the method's high potential for identifying significant sequences involved in immunogenicity. Furthermore, our study suggests that the Ab-MAPPs method can recognize the difference of a single amino acid residue between similar antibody sequences with different levels of T-cell proliferation activity and can identify potentially immunogenic peptides with high binding affinity to MHC II. In conclusion, Ab-MAPPs is useful for identifying the immunogenic sequences of therapeutic antibodies and will contribute to the design of therapeutic antibodies with low immunogenicity during the drug discovery stage.

Nature of tumour rejection antigens in ovarian cancer

Major progress in the analysis of human immune responses to cancer has been made through the molecular characterization of human tumour antigens. The development of therapeutic strategies for eliciting immune-mediated rejection of tumours has accelerated due to the elucidation of the molecular basis for tumour cell recognition and destruction by immune cells. Of the various human tumour antigens defined to date in ovarian cancer, the cancer-testis (CT) family of antigens have been studied extensively preclinically and clinically because of their testis-restricted expression in normal tissues and ability to elicit robust immune responses. Recent developments in cancer sequencing technologies offer a unique opportunity to identify tumour mutations with the highest likelihood of being expressed and recognized by the immune system. Such mutations, or neoantigens, could potentially serve as specific immune targets for T-cell-mediated destruction of cancer cells. This review will highlight current work in selecting tumour rejection antigens in ovarian cancer for improving the efficacy of immunotherapy.

Induction of protective cellular immune responses against experimental visceral leishmaniasis mediated by dendritic cells pulsed with the N-terminal domain of Leishmania infantum elongation factor-2 and CpG oligodeoxynucleotides

Leishmania elongation factor 2 (EF-2) has been previously identified as a T_H1-stimulatory protein. In this study, we assayed the protective potential of the N-terminal domain of EF-2 (N-LiEF-2, 1-357 aa) that has been predicted to contain several overlapping MHC class I and II-restricted epitopes injected in the form of dendritic cell (DC)-based vaccine. Ex vivo pulsing of DCs with the recombinant N-LiEF-2 domain along with CpG oligodeoxynucleotides (ODNs) resulted in their functional differentiation. BALB/c vaccinated with CpG-triggered DCs pulsed with N-LiEF-2 were found to be the most immune-reactive in terms of induction of DTH responses, increased T cell proliferation and IL-2 production. Moreover, vaccination induced antigen-specific T_H1 type immune response as evidenced by increased IFN-γ and TNFα levels followed by a significant increase of nitrite (NO) and reactive oxygen species (ROS) in splenocyte cultures. Vaccinated mice showed a pronounced decrease in parasite load in spleen and liver when challenged with L. infantum, increased expression of Stat1 and Tbx21 mRNA transcripts versus reduced expression of Foxp3 transcripts and were able to produce significantly elevated levels of IL-2, IFN-γ and TNFα but not IL-10 compared to non-vaccinated mice. Both antigen and parasite-specific CD4⁺ T and CD8⁺ T cells contributed to the IFN-γ production indicating that both subtypes contribute to the resistance to infection and correlated with robust nitrite generation, critical in controlling Leishmania infection. Together, these findings demonstrated the immunogenic as well as protective potential of the N-terminal domain of Leishmania EF-2 when given with CpG-triggered DCs representing a basis for the development of rationalized vaccine against leishmaniasis.

Rescue of Pompe disease in mice by AAV-mediated liver delivery of secretable acid α-glucosidase

Glycogen storage disease type II or Pompe disease is a severe neuromuscular disorder caused by mutations in the lysosomal enzyme, acid α-glucosidase (GAA), which result in pathological accumulation of glycogen throughout the body. Enzyme replacement therapy is available for Pompe disease; however, it has limited efficacy, has high immunogenicity, and fails to correct pathological glycogen accumulation in nervous tissue and skeletal muscle. Using bioinformatics analysis and protein engineering, we developed transgenes encoding GAA that could be expressed and secreted by hepatocytes. Then, we used adeno-associated virus (AAV) vectors optimized for hepatic expression to deliver the GAA transgenes to Gaa knockout (Gaa^-/-) mice, a model of Pompe disease. Therapeutic gene transfer to the liver rescued glycogen accumulation in muscle and the central nervous system, and ameliorated cardiac hypertrophy as well as muscle and respiratory dysfunction in the Gaa^-/- mice; mouse survival was also increased. Secretable GAA showed improved therapeutic efficacy and lower immunogenicity compared to nonengineered GAA. Scale-up to nonhuman primates, and modeling of GAA expression in primary human hepatocytes using hepatotropic AAV vectors, demonstrated the therapeutic potential of AAV vector-mediated liver expression of secretable GAA for treating pathological glycogen accumulation in multiple tissues in Pompe disease.

Improved methods for predicting peptide binding affinity to MHC class II molecules

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.

Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions

Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4⁺ T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions.

Designing B- and T-cell multi-epitope based subunit vaccine using immunoinformatics approach to control Zika virus infection

The Zika virus is a rapidly spreading Aedes mosquito-borne sickness, which creates an unanticipated linkage birth deformity and neurological turmoil. This study represents the use of the combinatorial immunoinformatics approach to develop a multiepitope subunit vaccine using the structural and nonstructural proteins of the Zika virus. The designed subunit vaccine consists of cytotoxic T-lymphocyte and helper T-lymphocyte epitopes accompanied by suitable adjuvant and linkers. The presence of humoral immune response specific B-cell epitopes was also confirmed by B-cell epitope mapping among vaccine protein. Further, the vaccine protein was characterized for its allergenicity, antigenicity, and physiochemical parameters and found to be safe and immunogenic. Molecular docking and molecular dynamics studies of the vaccine protein with the toll-like receptor-3 were performed to ensure the binding affinity and stability of their complex. Finally, in silico cloning was performed for the effective expression of vaccine construct in the microbial system (Escherichia coli K12 strain). Aforementioned approaches result in the multiepitope subunit vaccine which may have the ability to induce cellular as well as humoral immune response. Moreover, this study needs the experimental validation to prove the immunogenic and protective behavior of the developed subunit vaccine.

In silico prediction of T- and B-cell epitopes in PmpD: First step towards to the design of a Chlamydia trachomatis vaccine

BACKGROUND

Chlamydia trachomatis is the most common sexually transmitted bacterial infection globally. Currently, there are no vaccines available despite the efforts made to develop a protective one. Polymorphic membrane protein D (PmpD) is an attractive immunogen candidate as it is conserved among strains and it is target of neutralizing antibodies. However, its high molecular weight and its complex structure make it difficult to handle by recombinant DNA techniques. Our aim is to predict B-cell and T-cell epitopes of PmpD.

METHOD

A sequence (Genbank AAK69391.2) having 99-100% identity with various serovars of C. trachomatis was used for predictions. NetMHC and NetMHCII were used for T-cell epitope linked to MHC I or MHC II alleles prediction, respectively. BepiPred predicted linear B-cell epitopes. For three dimensional epitopes, PmpD was homology-modeled by Raptor X. Surface epitopes were predicted on its globular structure using DiscoTope.

RESULTS

NetMHC predicted 271 T-cell epitopes of 9-12aa with weak affinity, and 70 with strong affinity to MHC I molecules. NetMHCII predicted 2903 T-cell epitopes of 15aa with weak affinity, and 742 with strong affinity to MHC II molecules. Twenty four linear B-cell epitopes were predicted. Raptor X was able to model 91% of the three-dimensional structure whereas 57 residues of discontinuous epitopes were suggested by DiscoTope. Six regions containing B-cell and T-cell epitopes were identified by at least two predictors.

CONCLUSIONS

PmpD has potential B-cell and T-cell epitopes distributed throughout the sequence. Thus, several fragments were identified as valuable candidates for subunit vaccines against C. trachomatis.

An immunoinformatics approach to promiscuous peptide design for the Plasmodium falciparum erythrocyte membrane protein-1

Plasmodium falciparum erythrocyte membrane protein-1 (Pfemp-1), a variant adhesion molecule, can act as a key component of immunity against malaria. In the current selection of malaria vaccines, no efficient vaccines are available that can be employed for its proper treatment. Unfortunately, resistance to post-infection treatments is increasing and therefore there is a pressing need to develop an efficient vaccine. Peptide-based vaccines can be effective tools against malaria but HLA restriction is a major hindrance which can be conquered by using promiscuous peptides. In this work, we employed a combined in silico and experimental approach to identify promiscuous peptides for the treatment of malaria. At first, using the immunoinformatics approach, promiscuous peptides were predicted from two conserved domains, CIDR-1 and DBL-3γ, of the Pfemp-1 antigen. These peptides were selected on the basis of their predicted binding affinity with different HLA class-I & class-II alleles. A total of 13 peptides were selected based on their predicted IFN-γ and IL-4 induction ability as well as their hydrophobicity. Out of these 13, the peptide C6 was synthesised and experimentally evaluated for further rationalization, HLA-peptide complex modelling and binding interaction analysis. Interestingly, the peptide C6 (SFIHIYLYRNIRIQL) showed an encouraging immunological response and T-cell proliferation in the immunological assay. This valuable content can aid the better design of more potent and selective vaccine candidates against infectious diseases.

An automated benchmarking platform for MHC class II binding prediction methods

Motivation

Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research.

Results

With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method.

Availability and implementation

Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/.

Contact

mniel@bioinformatics.dtu.dk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of epitope-based vaccine candidates against the E antigen of the hepatitis B virus based on the B genotype sequence: An in silico and in vitro approach

Chronic hepatitis B virus infection is a worldwide health problem with no current effective strategy to achieve a cure. The Hepatitis B virus (HBV) E antigen (HBeAg) has a negative effect on the immune system and a therapeutic vaccine is a promising strategy in order to treat chronic virus infection. In this study, we analyzed and identified the MHC-I, MHC-II and B cell epitopes of the HBeAg based on a B genotype sequence of HBV using a bioinformatic approach and in vitro experiments. The computational approach provided us with four epitopes (LLWFHISCL, YLVSFGVWI, MQLFHLCLI, TVLEYLVSF) of the specific MHC-I allele HLA-A0201 that conformed to all criteria. Molecular docking and a peptide binding assay showed that epitope TVLEYLVSF had the lowest binding energy and epitope LLWFHISCL had the highest binding affinity to the HLA-A0201 molecule. An interferonγenzyme-linked immunospot assay and cytotoxicity assay revealed that epitope LLWFHISCL had the highest ability to induce and stimulate T cells. Furthermore, we determined four core peptides of MHC-II epitopes and a region of the B cell epitope. The epitopes and region identified in this research may be helpful in designing epitope-based vaccines and boosting the mechanism research of HBeAg and its effect on the immune system.

Proteome-wide mapping of immune features onto Plasmodium protein three-dimensional structures

Humoral immune responses against the malaria parasite are an important component of a protective immune response. Antibodies are often directed towards conformational epitopes, and the native structure of the antigenic region is usually critical for antibody recognition. We examined the structural features of various Plasmodium antigens that may impact on epitope location, by performing a comprehensive analysis of known and modelled structures from P. falciparum. Examining the location of known polymorphisms over all available structures, we observed a strong propensity for polymorphic residues to be exposed on the surface and to occur in particular secondary structure segments such as hydrogen-bonded turns. We also utilised established prediction algorithms for B-cell epitopes and MHC class II binding peptides, examining predicted epitopes in relation to known polymorphic sites within structured regions. Finally, we used the available structures to examine polymorphic hotspots and Tajima's D values using a spatial averaging approach. We identified a region of PfAMA1 involving both domains II and III under a high degree of balancing selection relative to the rest of the protein. In summary, we developed general methods for examining how sequence-based features relate to one another in three-dimensional space and applied these methods to key P. falciparum antigens.