Analysis and prediction of affinity of TAP binding peptides using cascade SVM

Differential modulation of mutant CALR and JAK2 V617F-driven oncogenesis by HLA genotype in myeloproliferative neoplasms

It has been demonstrated previously that human leukocyte antigen class I (HLA-I) and class II (HLA-II) alleles may modulate JAK2 V617F and CALR mutation (CALRmut)-associated oncogenesis in myeloproliferative neoplasms (MPNs). However, the role of immunogenetic factors in MPNs remains underexplored. We aimed to investigate the potential involvement of HLA genes in CALRmut+ MPNs. High-resolution genotyping of HLA-I and -II loci was conducted in 42 CALRmut+ and 158 JAK2 V617F+ MPN patients and 1,083 healthy controls. A global analysis of the diversity of HLA-I genotypes revealed no significant differences between CALRmut+ patients and controls. However, one HLA-I allele (C*06:02) showed an inverse correlation with presence of CALR mutation. A meta-analysis across independent cohorts and healthy individuals from the 1000 Genomes Project confirmed an inverse correlation between the presentation capabilities of the HLA-I loci for JAK2 V617F and CALRmut-derived peptides in both patients and healthy individuals. scRNA-Seq analysis revealed low expression of TAP1 and CIITA genes in CALRmut+ hematopoietic stem and progenitor cells. In conclusion, the HLA-I genotype differentially restricts JAK2 V617F and CALRmut-driven oncogenesis potentially explaining the mutual exclusivity of the two mutations and differences in their presentation latency. These findings have practical implications for the development of neoantigen-based vaccines in MPNs.

Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity

T and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R2 = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated in vitro. The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS .

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

DeepTAP: An RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens

The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap.

A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/).

Prediction of TAP Transport of Peptides with Variable Length Using TAPREG

CD8 T cells recognize short peptides, more frequently of nine residues, presented by class I major histocompatibility complex (MHC I) molecules in the cell surface of antigen-presenting cells. These epitope peptides are loaded onto MHC I molecules in the endoplasmic reticulum, where they are shuttled from the cytosol by the transporter associated with antigen processing (TAP) as such or as N-terminal extended precursors of up to 16 residues. In this chapter, we describe the use of TAPREG, a tool for predicting TAP binding affinity that has been enhanced to identify potential CD8 T cell epitope precursors transported by TAP. TAPREG is available for free public use at http://imed.med.ucm.es/Tools/tapreg/ .

The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies

Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8+ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.

Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions

Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.

Designing and development of epitope-based vaccines against Helicobacter pylori

Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.

In silico analysis of STX2a-PE15-P4A8 chimeric protein as a novel immunotoxin for cancer therapy

Today, the targeted therapies like the use of immunotoxins are increased which targeted specific antigens or receptors on the surface of tumor cells. Fibroblast growth factor-inducible 14 (Fn14) is a cytokine receptor which involves several intercellular signaling pathways and can be highly expressed in the surface of cancer cells. Since the cleavage of enzymatic domain of Pseudomonas exotoxin A (PE) occurs in one step by furin protease, we fused enzymatic subunit of Shiga-like toxin type 2a (Stx2a) with domain II and a portion of Ib of PE to increase the toxicity of Stx. Then, we genetically fused the Fv fragment of an anti-Fn14 monoclonal antibody (P4A8) to STX2a-PE15 and evaluated the STX2a-PE15-P4A8 chimeric protein as a new immunotoxin candidate. In silico analysis showed that the STX2a-PE15-P4A8 is a stable chimeric protein with high affinity to the Fn14 receptor. Despite, the STX2a-PE15-P4A8 can be bind to the B cell receptor, but it has been weakly presented by major histocompatibility complex molecules II (MHC-II). So, it may have a little immunogenicity. On the basis of our in-silico studies we predict that STX2a-PE15-P4A8 can be a good candidate for cancer immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00079-w.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

An immunoprophylactic evaluation of Ld-ODC derived HLA-A0201 restricted peptides against visceral leishmaniasis

Five (5) HLA-A 0201 restricted epitopes of ornithine decarboxylase derived from Leishmania donovani (Ld-ODC) were examined by reverse vaccinology to develop prophylactics against visceral leishmaniasis (VL). These consensus epitopes comprising (P1: RLMPSAHAI, P2: LLDQYQIHL, P3: GLYHSFNCI, P4: AVLEVLSAL and P5: RLPASPAAL) were observed and presented by diverse HLA alleles screened by immune-informatics tools. These epitopes were also observed for strong stability for appropriate immune response in in silico screening and molecular dynamics. Top five selected epitopes filtered from population coverage analysis and TAP binding affinity were identified and evaluated against treated cases of VL subjects. Experiments were run individually with synthetic peptides or as the cocktail of peptides. A major population of CD8+ T cells were predominantly IFN-γ producers but not the IL-10 cytokines and shown with granzyme-B activity. Therefore, it can be concluded that the screened HLA-A0201 restricted epitope hotspots derived from Leishmania ODC can trigger CD8+ T cells, which can skew other immune cells functions toward protection. However, a detailed analysis can explore its potentiality as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations.Communicated by Ramaswamy H. Sarma.

High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides

MOTIVATION

The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.

RESULTS

Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.

AVAILABILITY AND IMPLEMENTATION

Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Main Strategies for the Identification of Neoantigens

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

pssRNAit: A Web Server for Designing Effective and Specific Plant siRNAs with Genome-Wide Off-Target Assessment

We report an advanced web server, the plant-specific small noncoding RNA interference tool pssRNAit, which can be used to design a pool of small interfering RNAs (siRNAs) for highly effective, specific, and nontoxic gene silencing in plants. In developing this tool, we integrated the transcript dataset of plants, several rules governing gene silencing, and a series of computational models of the biological mechanism of the RNA interference (RNAi) pathway. The designed pool of siRNAs can be used to construct a long double-strand RNA and expressed through virus-induced gene silencing (VIGS) or synthetic transacting siRNA vectors for gene silencing. We demonstrated the performance of pssRNAit by designing and expressing the VIGS constructs to silence Phytoene desaturase (PDS) or a ribosomal protein-encoding gene, RPL10 (QM), in Nicotiana benthamiana We analyzed the expression levels of predicted intended-target and off-target genes using reverse transcription quantitative PCR. We further conducted an RNA-sequencing-based transcriptome analysis to assess genome-wide off-target gene silencing triggered by the fragments that were designed by pssRNAit, targeting different homologous regions of the PDS gene. Our analyses confirmed the high accuracy of siRNA constructs designed using pssRNAit The pssRNAit server, freely available at https://plantgrn.noble.org/pssRNAit/, supports the design of highly effective and specific RNAi, VIGS, or synthetic transacting siRNA constructs for high-throughput functional genomics and trait improvement in >160 plant species.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

An immunoinformatic approach driven by experimental proteomics: in silico design of a subunit candidate vaccine targeting secretory proteins of Leishmania donovani amastigotes

Background

Visceral leishmaniasis (VL) caused by dimorphic Leishmania species is a parasitic disease with high socioeconomic burden in endemic areas worldwide. Sustaining control of VL in terms of proper and prevailing immunity development is a global necessity amid unavailability of a prophylactic vaccine. Screening of experimental proteome of the human disease propagating form of Leishmania donovani (amastigote) can be more pragmatic for in silico mining of novel vaccine candidates.

Methods

By using an immunoinformatic approach, CD4+ and CD8+ T cell-specific epitopes from experimentally reported L. donovani proteins having secretory potential and increased abundance in amastigotes were screened. A chimera linked with a Toll-like receptor 4 (TLR4) peptide adjuvant was constructed and evaluated for physicochemical characteristics, binding interaction with TLR4 in simulated physiological condition and the trend of immune response following hypothetical immunization.

Results

Selected epitopes from physiologically important L. donovani proteins were found mostly conserved in L. infantum, covering theoretically more than 98% of the global population. The multi-epitope chimeric vaccine was predicted as stable, antigenic and non-allergenic. Structural analysis of vaccine-TLR4 receptor docked complex and its molecular dynamics simulation suggest sufficiently stable binding interface along with prospect of non-canonical receptor activation. Simulation dynamics of immune response following hypothetical immunization indicate active and memory B as well as CD4+ T cell generation potential, and likely chance of a more Th1 polarized response.

Conclusions

The methodological approach and results from this study could facilitate more informed screening and selection of candidate antigenic proteins for entry into vaccine production pipeline in future to control human VL.

Study of the Binding Pattern of HLA Class I Alleles of Indian Frequency and cTAP Binding Peptide for Chikungunya Vaccine Development

Chikungunya is a mosquito-borne disease, caused by the member of the Togaviridae family belongs to the genus alphavirus, making it a major threat in all developing countries as well as some developed countries. The mosquito acts as a vector for the disease and carries the CHIK-Virus. To date there is no direct treatment available and that demands the development of more effective vaccines. In this study author employed Immune Epitope Database and Analysis Resource, a machine learning-based algorithm principally working on the Artificial Neural Network (ANN) algorithm, also known as (IEDB-ANN) for the prediction and analysis of Epitopes. A total of 173 epitopes were identified on the basis of IC50 values, among them 40 epitopes were found, sharing part with the linear B-cell epitopes and exposed to the cTAP1protein, and out of 40, 6 epitopes were noticed to show interactions with the cTAP with their binding energy ranging from - 3.61 to - 1.22 kcal/mol. The six epitopes identified were exposed to the HLA class I alleles and from this all revealed interaction with the HLA alleles and minimum binding energy that ranges from - 4.12 to - 5.88 kcal/mol. Besides, two T cell epitopes i.e. 145KVFTGVYPE153 and 395STVPVAPPR403 were found most promiscuous candidates. These promiscuous epitopes-HLA complexes were further analyzed by the molecular dynamics simulation to check the stability of the complex. Results obtained from this study suggest that the identified epitopes i.e. and 395 STVPVAPPR 403 , are likely to be capable of passing through the lumen of ER to bind withthe HLA class I allele and provide new insights and potential application in the designing and development of peptide-based vaccine candidate for the treatment of chikungunya.

Analysis of Secondary Structure Biases in Naturally Presented HLA-I Ligands

Recent clinical developments in antitumor immunotherapy involving T-cell related therapeutics have led to a renewed interest for human leukocyte antigen class I (HLA-I) binding peptides, given their potential use as peptide vaccines. Databases of HLA-I binding peptides hold therefore information on therapeutic targets essential for understanding immunity. In this work, we use in depth and accurate HLA-I peptidomics datasets determined by mass-spectrometry (MS) and analyze properties of the HLA-I binding peptides with structure-based computational approaches. HLA-I binding peptides are studied grouping all alleles together or in allotype-specific contexts. We capitalize on the increasing number of structurally determined proteins to (1) map the 3D structure of HLA-I binding peptides into the source proteins for analyzing their secondary structure and solvent accessibility in the protein context, and (2) search for potential differences between these properties in HLA-I binding peptides and in a reference dataset of HLA-I motif-like peptides. This is performed by an in-house developed heuristic search that considers peptides across all the human proteome and converges to a collection of peptides that exhibit exactly the same motif as the HLA-I peptides. Our results, based on 9-mers matched to protein 3D structures, clearly show enriched sampling for HLA-I presentation of helical fragments in the source proteins. This enrichment is significant, as compared to 9-mer HLA-I motif-like peptides, and is not entirely explained by the helical propensity of the preferred residues in the HLA-I motifs. We give possible hypothesis for the secondary structure biases observed in HLA-I peptides. This contribution is of potential interest for researchers working in the field of antigen presentation and proteolysis. This knowledge refines the understanding of the rules governing antigen presentation and could be added to the parameters of the current peptide-MHC class I binding predictors to increase their antigen predictive ability.

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.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

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.

Systems Immunology: Learning the Rules of the Immune System

Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.

Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum

Background

In the current scenario, designing of world-wide effective malaria vaccine against Plasmodium falciparum remain challenging despite the significant progress has been made in last few decades. Conventional vaccinology (isolate, inactivate and inject) approaches are time consuming, laborious and expensive; therefore, the use of computational vaccinology tools are imperative, which can facilitate the design of new and promising vaccine candidates.

Results

In current investigation, initially 5548 proteins of P. falciparum genome were carefully chosen for the incidence of signal peptide/ anchor using SignalP4.0 tool that resulted into 640 surface linked proteins (SLP). Out of these SLP, only 17 were predicted to contain GPI-anchors using PredGPI tool in which further 5 proteins were considered as malarial antigenic adhesins by MAAP and VaxiJen programs, respectively. In the subsequent step, T cell epitopes of 5 genome derived predicted antigenic adhesins (GDPAA) and 5 randomly selected known malarial adhesins (RSKMA) were analysed employing MHC class I and II tools of IEDB analysis resource. Finally, VaxiJen scored T cell epitopes from each antigen were considered for prediction of population coverage (PPC) analysis in the world-wide population including malaria endemic regions. The validation of the present in silico strategy was carried out by comparing the PPC of combined (MHC class I and II) predicted epitope ensemble among GDPAA (99.97%), RSKMA (99.90%) and experimentally known epitopes (EKE) of P. falciparum (97.72%) pertaining to world-wide human population.

Conclusions

The present study systematically screened 5 potential protective antigens from P. falciparum genome using bioinformatics tools. Interestingly, these GDPAA, RSKMA and EKE of P. falciparum epitope ensembles forecasted to contain highly promiscuous T cell epitopes, which are potentially effective for most of the world-wide human population with malaria endemic regions. Therefore, these epitope ensembles could be considered in near future for novel and significantly effective vaccine candidate against malaria.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2482-x) contains supplementary material, which is available to authorized users.

Identification of B- and T-cell epitopes on HtrA protein of Orientia tsutsugamushi

Orientia tsutsugamushi, a cause of scrub typhus is emerging as an important pathogen in several parts of the tropics. The control of this infection relies on rapid diagnosis, specific treatment, and prevention through vector control. Development of a vaccine for human use would be very important as a public health measure. Antibody and T-cell response have been found to be important in the protection against scrub typhus. This study was undertaken to predict the peptide vaccine that elicits both B- and T-cell immunity. The outer-membrane protein, 47-kDa high-temperature requirement A was used as the target protein for the identification of protective antigen(s). Using BepiPred2 program, the potential B-cell epitope PNSSWGRYGLKMGLR with high conservation among O. tsutsugamushi and the maximum surface exposed residues was identified. Using IEDB, NetMHCpan, and NetCTL programs, T-cell epitopes MLNELTPEL and VTNGIISSK were identified. These peptides were found to have promiscuous class-I major histocompatibility complex (MHC) binding affinity to MHC supertypes and high proteasomal cleavage, transporter associated with antigen processing prediction, and antigenicity scores. In the I-TASSER generated model, the C-score was -0.69 and the estimated TM-score was 0.63 ± 0.14. The location of the epitope in the 3D model was external. Therefore, an antibody to this outer-membrane protein epitope could opsonize the bacterium for clearance by the reticuloendothelial system. The T-cell epitopes would generate T-helper function. The B-cell epitope(s) identified could be evaluated as antigen(s) in immunodiagnostic assays. This cocktail of three peptides would elicit both B- and T-cell immune response with a suitable adjuvant and serve as a vaccine candidate.

A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules

Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.

The perfect personalized cancer therapy: cancer vaccines against neoantigens

In the advent of Immune Checkpoint inhibitors (ICI) and of CAR-T adoptive T-cells, the new frontier in Oncology is Cancer Immunotherapy because of its ability to provide long term clinical benefit in metastatic disease in several solid and liquid tumor types. It is now clear that ICI acts by unmasking preexisting immune responses as well as by inducing de novo responses against tumor neoantigens. Thanks to theprogress made in genomics technologies and the evolution of bioinformatics, neoantigens represent ideal targets, due to their specific expression in cancer tissue and the potential lack of side effects. In this review, we discuss the promise of preclinical and clinical results with mutation-derived neoantigen cancer vaccines (NCVs) along with the current limitations from bioinformatics prediction to manufacturing an effective new therapeutic approach.

Perpetual complexity: predicting human CD8+ T-cell responses to pathogenic peptides

The accurate prediction of human CD8⁺ T-cell epitopes has great potential clinical and translational implications in the context of infection, cancer and autoimmunity. Prediction algorithms have traditionally focused on calculated peptide affinity for the binding groove of MHC-I. However, over the years it has become increasingly clear that the ultimate T-cell recognition of MHC-I-bound peptides is governed by many contributing factors within the complex antigen presentation pathway. Recent advances in next-generation sequencing and immunnopeptidomics have increased the precision of HLA-I sub-allele classification, and have led to the discovery of peptide processing events and individual allele-specific binding preferences. Here, we review some of the discoveries that initiated the development of peptide prediction algorithms, and outline some of the current available online tools for CD8⁺ T-cell epitope prediction.

Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide

Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.

Design of peptide-based epitope vaccine and further binding site scrutiny led to groundswell in drug discovery against Lassa virus

Lassa virus (LASV) is responsible for an acute viral hemorrhagic fever known as Lassa fever. Sequence analyses of LASV proteome identified the most immunogenic protein that led to predict both T-cell and B-cell epitopes and further target and binding site depiction could allow novel drug findings for drug discovery field against this virus. To induce both humoral and cell-mediated immunity peptide sequence SSNLYKGVY, conserved region 41-49 amino acids were found as the most potential B-cell and T-cell epitopes, respectively. The peptide sequence might intermingle with 17 HLA-I and 16 HLA-II molecules, also cover 49.15-96.82% population coverage within the common people of different countries where Lassa virus is endemic. To ensure the binding affinity to both HLA-I and HLA-II molecules were employed in docking simulation with suggested epitope sequence. Further the predicted 3D structure of the most immunogenic protein was analyzed to reveal out the binding site for the drug design against Lassa Virus. Herein, sequence analyses of proteome identified the most immunogenic protein that led to predict both T-cell and B-cell epitopes and further target and binding site depiction could allow novel drug findings for drug discovery field against this virus.

The unequivocal preponderance of biocomputation in clinical virology

Bioinformatics and computer based data simulation and modeling are captivating biological research, delivering great results already and promising to deliver more. As biological research is a complex, intricate, diverse field, any available support is gladly taken. With recent outbreaks and epidemics, pathogens are a constant threat to the global economy and security. Virus related plagues are somehow the most difficult to handle. Biocomputation has provided appreciable help in resolving clinical virology related issues. This review, for the first time, surveys the current status of the role of computation in virus related research. Advances made in the fields of clinical virology, antiviral drug design, viral immunology and viral oncology, through input from biocomputation, have been discussed. The amount of progress made and the software platforms available are consolidated in this review. The limitations of computation based methods are presented. Finally, the challenges facing the future of biocomputation in clinical virology are speculated upon.

Biocomputation in clinical virology.

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

In Silico Validation of D7 Salivary Protein-derived B- and T-cell Epitopes of Aedes aegypti as Potential Vaccine to Prevent Transmission of Flaviviruses and Togaviruses to Humans

Mosquito (Aedes aegyptii) salivary proteins play a crucial role in facilitating viral transmission from vector-to-host due to their role in facilitating the "blood meal" of the vector. Three main proteins, D7, aegyptin and Sialokinin play a role in this process. Using in-silico programs, we identified B- and T-cell epitopes in the mosquito salivary proteins D7 long and short form. T-cell epitopes with high affinity to the most prevalent HLA MHC class-I supertypes among different population groups was chosen. It is our postulate that these epitopes could be successful in eliciting B and T cell responses, which would decrease the vector blood meal efficiency and hence protect against host infection by certain viruses. These include causative agents like Dengue viruses, Chikungunya virus, Zika and Yellow fever viruses. These viruses are of major public health importance in several countries in the Americas, Asia and Africa. Experimental evidence exists in previously published literature showing the protective effect of antibodies to certain salivary proteins in susceptible hosts. A novel approach of immunizing humans against the vector proteins to reduce transmission of viruses is now under investigation in several laboratories. We have identified the following two B cell epitopes LAALHVTAAPLWDAKDPEQF one from D7L and the other TSEYPDRQNQIEELNKLCKN from D7S. Likewise, two T cell epitopes MTSKNELDV one from D7L and the other YILCKASAF from D7S with affinity to the predominant MHC class-I supertypes were identified towards evaluation as potential vaccine.

IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response

IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

Immunoinformatics Approach in Designing Epitope-based Vaccine Against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b)

Meningitis infection is one of the major threats during Hajj season in Mecca. Meningitis vaccines are available, but their uses are limited in some countries due to religious reasons. Furthermore, they only give protection to certain serogroups, not to all types of meningitis-inducing bacteria. Recently, research on epitope-based vaccines has been developed intensively. Such vaccines have potential advantages over conventional vaccines in that they are safer to use and well responded to the antibody. In this study, we developed epitope-based vaccine candidates against various meningitis-inducing bacteria, including Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae type b. The epitopes were selected from their protein of polysaccharide capsule. B-cell epitopes were predicted by using BCPred, while T-cell epitope for major histocompatibility complex (MHC) class I was predicted using PAProC, TAPPred, and Immune Epitope Database. Immune Epitope Database was also used to predict T-cell epitope for MHC class II. Population coverage and molecular docking simulation were predicted against previously generated epitope vaccine candidates. The best candidates for MHC class I- and class II-restricted T-cell epitopes were MQYGDKTTF, MKEQNTLEI, ECTEGEPDY, DLSIVVPIY, YPMAMMWRNASNRAI, TLQMTLLGIVPNLNK, ETSLHHIPGISNYFI, and SLLYILEKNAEMEFD, which showed 80% population coverage. The complexes of class I T-cell epitopes–HLA-C*03:03 and class II T-cell epitopes–HLA-DRB1*11:01 showed better affinity than standards as evaluated from their ΔGbinding value and the binding interaction between epitopes and HLA molecules. These peptide constructs may further be undergone in vitro and in vivo testings for the development of targeted vaccine against meningitis infection.

The Value of Online Algorithms to Predict T-Cell Ligands Created by Genetic Variants

Allogeneic stem cell transplantation can be a curative treatment for hematological malignancies. After HLA-matched allogeneic stem cell transplantation, beneficial anti-tumor immunity as well as detrimental side-effects can develop due to donor-derived T-cells recognizing polymorphic peptides that are presented by HLA on patient cells. Polymorphic peptides on patient cells that are recognized by specific T-cells are called minor histocompatibility antigens (MiHA), while the respective peptides in donor cells are allelic variants. MiHA can be identified by reverse strategies in which large sets of peptides are screened for T-cell recognition. In these strategies, selection of peptides by prediction algorithms may be relevant to increase the efficiency of MiHA discovery. We investigated the value of online prediction algorithms for MiHA discovery and determined the in silico characteristics of 68 autosomal HLA class I-restricted MiHA that have been identified as natural ligands by forward strategies in which T-cells from in vivo immune responses after allogeneic stem cell transplantation are used to identify the antigen. Our analysis showed that HLA class I binding was accurately predicted for 87% of MiHA of which a relatively large proportion of peptides had strong binding affinity (56%). Weak binding affinity was also predicted for a considerable number of antigens (31%) and the remaining 13% of MiHA were not predicted as HLA class I binding peptides. Besides prediction for HLA class I binding, none of the other online algorithms significantly contributed to MiHA characterization. Furthermore, we demonstrated that the majority of MiHA do not differ from their allelic variants in in silico characteristics, suggesting that allelic variants can potentially be processed and presented on the cell surface. In conclusion, our analyses revealed the in silico characteristics of 68 HLA class I-restricted MiHA and explored the value of online algorithms to predict T-cell ligands that are created by genetic variants.

Kirshner J, MacDonald D, Thurston G, C. Lin H, J. Murphy A, Zhang W. In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy

BACKGROUND

Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues.

RESULTS

We developed a novel computational strategy for identifying potential peptide-HLA cancer targets and evaluating the likelihood of off-target toxicity associated with these targets. Our strategy combines sequence-based and structure-based approaches in a unique way to predict potential off-targets. The focus of our work is on the complexes involving the most frequent HLA class I allele HLA-A*02:01. Using our strategy, we predicted the off-target toxicity observed in past clinical trials. We employed it to perform a first-ever comprehensive exploration of the human peptidome to identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas) and GTEx (Gene Tissue Expression), and structural data from PDB (Protein Data Bank). We have thus identified a list of 627 peptide-HLA complexes across various TCGA cancer types.

CONCLUSION

Peptide-HLA complexes identified using our novel strategy could enable discovery of cancer-specific targets for engineered T-cells or antibody based therapy with minimal off-target toxicity.

In silico analysis and modeling of ACP-MIP-PilQ chimeric antigen from Neisseria meningitidis serogroup B

BACKGROUND

Neisseria meningitidis, a life-threatening human pathogen with the potential to cause large epidemics, can be isolated from the nasopharynx of 5-15% of adults. The aim of the current study was to evaluate biophysical and biochemical properties and immunological aspects of chimeric acyl-carrier protein-macrophage infectivity potentiator protein-type IV pilus biogenesis protein antigen (ACP-MIP-PilQ) from N. meningitidis serogroup B strain.

METHODS

Biochemical properties and multiple alignments were predicted by appropriate web servers. Secondary molecular structures were predicted based on Chou and Fasman, Garnier-Osguthorpe-Robson, and Neural Network methods. Tertiary modeling elucidated conformational properties of the chimeric protein. Proteasome cleavage and transporter associated with antigen processing (TAP) binding sites, and T- and B-cell antigenic epitopes, were predicted using bioinformatic web servers.

RESULTS

Based on our in silico and immunoinformatics analyses, the ACP-MIP-PilQ protein (AMP) can induce high-level cross-strain bactericidal activity. In addition, several immune proteasomal cleavage sites were detected. The 22 epitopes associated with MHC class I and class II (DR) alleles were confirmed in the AMP. Thirty linear B-cell epitopes as antigenic regions were predicted from the full-length protein.

CONCLUSION

All predicted properties of the AMP indicate it could be a good candidate for further immunological in vitro and in vivo studies.

Design and Antigenic Epitopes Prediction of a New Trial Recombinant Multiepitopic Rotaviral Vaccine: In Silico Analyses

Rotavirus is the major etiologic factor of severe diarrheal disease. Natural infection provides protection against subsequent rotavirus infection and diarrhea. This research presents a new vaccine designed based on computational models. In this study, three types of epitopes are considered-linear, conformational, and combinational-in a proposed model protein. Several studies on rotavirus vaccines have shown that VP6 and VP4 proteins are good candidates for vaccine production. In the present study, a fusion protein was designed as a new generation of rotavirus vaccines by bioinformatics analyses. This model-based study using ABCpred, BCPREDS, Bcepred, and Ellipro web servers showed that the peptide presented in this article has the necessary properties to act as a vaccine. Prediction of linear B-cell epitopes of peptides is helpful to investigate whether these peptides are able to activate humoral immunity.

Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning

Background

T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.

Methods

In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set.

Results

Two datasets named ‘IMMA2’ and ‘PAAQD’ are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant.

Conclusions

The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in S1 File.

A novel "priming-boosting" strategy for immune interventions in cervical cancer

Despite the encouraging development of a preventive vaccine for human papillomavirus (HPV), it cannot improve ongoing infections. Therefore, a new vaccine is urgently needed that can prevent and treat cervical cancer, and cure pre-cancerous lesions. In this study, we constructed two peptide-based vaccines. The first was a short-term, long-peptide (ST-LP) vaccine that simultaneously targeted three key carcinogenic epitopes (E5-E6-E7) on HPV16. We tested this vaccine in murine TC-1 cells infected with a recombinant adeno-associated virus (rAAV) fused with HPV16E5 DNA (rTC-1 cells), which served as a cell model; we also tested it in immune-competent mice loaded with rTC-1 cells, which served as an ectopic tumor model. The ST-LP injections resulted in strong, cell-mediated immunity, capable of attacking and eliminating abnormal antigen-bearing cells. Furthermore, to prolong immunogenic capability, we designed a unique rAAV that encoded the three predicted epitopes for a second, long-term, long-peptide (LT-LP) vaccine. Moreover, we used a new immune strategy of continuous re-injections, where three ST-LP injections were performed at one-week intervals (days 0, 7, 14), then one LT-LP injection was performed on day 120. Our in vitro and in vivo studies revealed that this strategy could boost the immune response to produce longer and stronger protection against target cells, and mice were thoroughly protected from tumor growth. Our results showed that priming the immune system with the ST-LP vaccine, followed by boosting the immune system with the LT-LP vaccine could generate a rapid, robust, durable cytotoxic T-lymphocyte response to HPV16-positive tumors.