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

Reverse vaccinology-based multi-epitope vaccine design against Indian group A rotavirus targeting VP7, VP4, and VP6 proteins

Rotavirus, a primary contributor to severe cases of infantile gastroenteritis on a global scale, results in significant morbidity and mortality in the under-five population, particularly in middle to low-income countries, including India. WHO-approved live-attenuated vaccines are linked to a heightened susceptibility to intussusception and exhibit low efficacy, primarily attributed to the high genetic diversity of rotavirus, varying over time and across different geographic regions. Herein, molecular data on Indian rotavirus A (RVA) has been reviewed through phylogenetic analysis, revealing G1P[8] to be the prevalent strain of RVA in India. The conserved capsid protein sequences of VP7, VP4 and VP6 were used to examine helper T lymphocyte, cytotoxic T lymphocyte and linear B-cell epitopes. Twenty epitopes were identified after evaluation of factors such as antigenicity, non-allergenicity, non-toxicity, and stability. These epitopes were then interconnected using suitable linkers and an N-terminal beta defensin adjuvant. The in silico designed vaccine exhibited structural stability and interactions with integrins (αvβ3 and αIIbβ3) and toll-like receptors (TLR2 and TLR4) indicated by docking and normal mode analyses. The immune simulation profile of the designed RVA multiepitope vaccine exhibited its potential to trigger humoral as well as cell-mediated immunity, indicating that it is a promising immunogen. These computational findings indicate potential efficacy of the designed vaccine against rotavirus infection.

A computational approach to developing a multi-epitope vaccine for combating Pseudomonas aeruginosa-induced pneumonia and sepsis

Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine's composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa-related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model's authenticity once the protein's three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.

Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach

Canine distemper virus (CDV) affects many domestic and wild animals. Variations among CDV genome linages could lead to vaccination failure. To date, there are several vaccine alternatives, such as a modified live virus and a recombinant vaccine; however, most of these alternatives are based on the ancestral strain Onderstepoort, which has not been circulating for years. Vaccine failures and the need to update vaccines have been widely discussed, and the development of new vaccine candidates is necessary to reduce circulation and mortality. Current vaccination alternatives cannot be used in wildlife animals due to the lack of safety data for most of the species, in addition to the insufficient immune response against circulating strains worldwide in domestic species. Computational tools, including peptide-based therapies, have become essential for developing new-generation vaccines for diverse models. In this work, a peptide-based vaccine candidate with a peptide library derived from CDV H and F protein consensus sequences was constructed employing computational tools. The molecular docking and dynamics of the selected peptides with canine MHC-I and MHC-II and with TLR-2 and TLR-4 were evaluated. In silico safety was assayed through determination of antigenicity, allergenicity, toxicity potential, and homologous canine peptides. Additionally, in vitro safety was also evaluated through cytotoxicity in cell lines and canine peripheral blood mononuclear cells (cPBMCs) and through a hemolysis potential assay using canine red blood cells. A multiepitope CDV polypeptide was constructed, synthetized, and evaluated in silico and in vitro by employing the most promising peptides for comparison with single CDV immunogenic peptides. Our findings suggest that predicting immunogenic CDV peptides derived from most antigenic CDV proteins could aid in the development of new vaccine candidates, such as multiple single CDV peptides and multiepitope CDV polypeptides, that are safe in vitro and optimized in silico. In vivo studies are being conducted to validate potential vaccines that may be effective in preventing CDV infection in domestic and wild animals.

GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction

Accurately predicting the binding affinities between Human Leukocyte Antigen (HLA) molecules and peptides is a crucial step in understanding the adaptive immune response. This knowledge can have important implications for the development of effective vaccines and the design of targeted immunotherapies. Existing sequence-based methods are insufficient to capture the structure information. Besides, the current methods lack model interpretability, which hinder revealing the key binding amino acids between the two molecules. To address these limitations, we proposed an interpretable graph convolutional neural network (GCNN) based prediction method named GIHP. Considering the size differences between HLA and short peptides, GIHP represent HLA structure as amino acid-level graph while represent peptide SMILE string as atom-level graph. For interpretation, we design a novel visual explanation method, gradient weighted activation mapping (Grad-WAM), for identifying key binding residues. GIHP achieved better prediction accuracy than state-of-the-art methods across various datasets. According to current research findings, key HLA-peptide binding residues mutations directly impact immunotherapy efficacy. Therefore, we verified those highlighted key residues to see whether they can significantly distinguish immunotherapy patient groups. We have verified that the identified functional residues can successfully separate patient survival groups across breast, bladder, and pan-cancer datasets. Results demonstrate that GIHP improves the accuracy and interpretation capabilities of HLA-peptide prediction, and the findings of this study can be used to guide personalized cancer immunotherapy treatment. Codes and datasets are publicly accessible at: https://github.com/sdustSu/GIHP.

MONET: a database for prediction of neoantigens derived from microsatellite loci

Background

Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).

Method

MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.

Results

MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.

Conclusion

MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Comprehensive profiling of cancer neoantigens from aberrant RNA splicing

BACKGROUND

Cancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a computational framework, REAL-neo, for identification, quality control, and prioritization of both class-I and class-II human leucocyte antigen (HLA)-presented neoantigens resulting from somatic single-nucleotide mutations, small insertions and deletions, and gene fusions. In this study, we developed a new module, SPLICE-neo, to identify neoantigens from aberrant RNA transcripts from two distinct sources: (1) DNA mutations within splice sites and (2) de novo RNA aberrant splicings.

METHODS

First, SPLICE-neo was used to profile all DNA splice-site mutations in 11,892 tumors from The Cancer Genome Atlas (TCGA) and identified 11 profiles of splicing donor or acceptor site gains or losses. Transcript isoforms resulting from the top seven most frequent profiles were computed using novel logic models. Second, SPLICE-neo identified de novo RNA splicing events using RNA sequencing reads mapped to novel exon junctions from either single, double, or multiple exon-skipping events. The aberrant transcripts from both sources were then ranked based on isoform expression levels and z-scores assuming that individual aberrant splicing events are rare. Finally, top-ranked novel isoforms were translated into protein, and the resulting neoepitopes were evaluated for neoantigen potential using REAL-neo. The top splicing neoantigen candidates binding to HLA-A*02:01 were validated using in vitro T2 binding assays.

RESULTS

We identified abundant splicing neoantigens in four representative TCGA cancers: BRCA, LUAD, LUSC, and LIHC. In addition to their substantial contribution to neoantigen load, several splicing neoantigens were potent tumor antigens with stronger bindings to HLA compared with the positive control of antigens from influenza virus.

CONCLUSIONS

SPLICE-neo is the first tool to comprehensively identify and prioritize splicing neoantigens from both DNA splice-site mutations and de novo RNA aberrant splicings. There are two major advances of SPLICE-neo. First, we developed novel logic models that assemble and prioritize full-length aberrant transcripts from DNA splice-site mutations. Second, SPLICE-neo can identify exon-skipping events involving more than two exons, which account for a quarter to one-third of all skipping events.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Engineering Antigen-Specific Tolerance to an Artificial Protein Hydrogel

Artificial protein hydrogels are an emerging class of biomaterials with numerous prospective applications in tissue engineering and regenerative medicine. These materials are likely to be immunogenic due to their frequent incorporation of novel amino acid sequence domains, which often serve a functional role within the material itself. We engineered injectable "self" and "nonself" artificial protein hydrogels, which were predicted to have divergent immune outcomes in vivo on the basis of their primary amino acid sequence. Following implantation in mouse, the nonself gels raised significantly higher antigel antibody titers than the corresponding self gels. Prophylactic administration of a fusion antibody targeting the nonself hydrogel epitopes to DEC-205, an endocytic receptor involved in Treg induction, fully suppressed the elevated antibody titer against the nonself gels. These results suggest that the clinical immune response to artificial protein biomaterials, including those that contain highly antigenic sequence domains, can be tuned through the induction of antigen-specific tolerance.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

An Immunoinformatics-Based Study of Mycobacterium tuberculosis Region of Difference-2 Uncharacterized Protein (Rv1987) as a Potential Subunit Vaccine Candidate for Preliminary Ex Vivo Analysis

Mycobacterium tuberculosis (Mtb) is the pathogen that causes tuberculosis and develops resistance to many of the existing drugs. The sole licensed TB vaccine, BCG, is unable to provide a comprehensive defense. So, it is crucial to maintain the immunological response to eliminate tuberculosis. Our previous in silico study reported five uncharacterized proteins as potential vaccine antigens. In this article, we considered the uncharacterized Mtb H37Rv regions of difference (RD-2) Rv1987 protein as a promising vaccine candidate. The vaccine quality of the protein was analyzed using reverse vaccinology and immunoinformatics-based quality-checking parameters followed by an ex vivo preliminary investigation. In silico analysis of Rv1987 protein predicted it as surface localized, secretory, single helix, antigenic, non-allergenic, and non-homologous to the host protein. Immunoinformatics analysis of Rv1987 by CD4 + and CD8 + T-cells via MHC-I and MHC-II binding affinity and presence of B-cell epitope predicted its immunogenicity. The docked complex analysis of the 3D model structure of the protein with immune cell receptor TLR-4 revealed the protein's capability for potential interaction. Furthermore, the target protein-encoded gene Rv1987 was cloned, over-expressed, purified, and analyzed by mass spectrometry (MS) to report the target peptides. The qRT-PCR gene expression analysis shows that it is capable of activating macrophages and significantly increasing the production of a number of key cytokines (TNF-α, IL-1β, and IL-10). Our in-silico analysis and ex vivo preliminary investigations revealed the immunogenic potential of the target protein. These findings suggest that the Rv1987 be undertaken as a potent subunit vaccine antigen and that further animal model immuno-modulation studies would boost the novel TB vaccine discovery and/or BCG vaccine supplement pipeline.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

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

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

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

Self-assembled peptide/polymer hybrid nanoplatform for cancer immunostimulating therapies

Integrating peptide epitopes in self-assembling materials is a successful strategy to obtain nanovaccines with high antigen density and improved efficacy. In this study, self-assembling peptides containing MAGE-A3/PADRE epitopes were designed to generate functional therapeutic nanovaccines. To achieve higher stability, peptide/polymer hybrid nanoparticles were formulated by controlled self-assembly of the engineered peptides. The nanoparticles showed good biocompatibility to both human red blood- and dendritic cells. Incubation of the nanoparticles with immature dendritic cells triggered immune effects that ultimately activated CD8 + cells. The antigen-specific and IgG antibody responses of healthy C57BL/6 mice vaccinated with the nanoparticles were analyzed. The in vivo results indicate a specific response to the nanovaccines, mainly mediated through a cellular pathway. This research indicates that the immunogenicity of peptide epitope vaccines can be effectively enhanced by developing self-assembled peptide-polymer hybrid nanostructures.

Structure-aware deep model for MHC-II peptide binding affinity prediction

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.

T and B cell epitope analysis for the immunogenicity evaluation and mitigation of antibody-based therapeutics

The surge in the clinical use of therapeutic antibodies has reshaped the landscape of pharmaceutical therapy for many diseases, including rare and challenging conditions. However, the administration of exogenous biologics could potentially trigger unwanted immune responses such as generation of anti-drug antibodies (ADAs). Real-world experiences have illuminated the clear correlation between the ADA occurrence and unsatisfactory therapeutic outcomes as well as immune-related adverse events. By retrospectively examining research involving immunogenicity analysis, we noticed the growing emphasis on elucidating the immunogenic epitope profiles of antibody-based therapeutics aiming for mechanistic understanding the immunogenicity generation and, ideally, mitigating the risks. As such, we have comprehensively summarized here the progress in both experimental and computational methodologies for the characterization of T and B cell epitopes of therapeutics. Furthermore, the successful practice of epitope-driven deimmunization of biotherapeutics is exceptionally highlighted in this article.

Comparison of the somatic genomic landscape between central- and peripheral-type non-small cell lung cancer

OBJECTIVE

Lung cancer is classified into central and peripheral types based on the anatomic location. The present study aimed to explore the distinct patterns of genomic alterations between central- and peripheral-type non-small cell lung cancers (NSCLCs) with negative driver genes and identify potential driver genes and biomarkers to improve therapy strategies for NSCLC.

METHODS

Whole-exome sequencing (WES) was performed with 182 tumor/control pairs of samples from 145 Chinese NSCLC patients without EGFR, ALK, or ROS1 alterations. Significantly mutated genes (SMGs) and somatic copy number alterations (SCNAs) were identified. Subsequently, tumor mutation burden (TMB), weighted genome integrity index (wGII), copy number alteration (CNA) burden, Shannon diversity index (SDI), intratumor heterogeneity (ITH), neoantigen load (NAL), and clonal variations were evaluated in central- and peripheral-type NSCLCs. Furthermore, mutational signature analysis and survival analysis were performed.

RESULTS

TP53 was the most frequently mutated gene in NSCLC and more frequently mutated in central-type NSCLC. Higher wGII, ITH, and SDI were found in central-type lung adenocarcinoma (LUAD) than in peripheral-type LUAD. The NAL of central-type lung squamous cell carcinoma (LUSC) with stage III/IV was significantly higher than that of peripheral-type LUSC. Mutational signature analysis revealed that SBS10b, SBS24, and ID7 were significantly different in central- and peripheral-type NSCLCs. Furthermore, central-type NSCLC was found to evolve at a higher level with fewer clones and more subclones, particularly in central-type LUSC. Survival analysis revealed that TMB, CNA burden, NAL, subclonal driver mutations, and subclonal mutations were negatively related to the overall survival (OS) and the progression-free survival (PFS) of central-type LUAD.

CONCLUSIONS

Central-type NSCLC tended to evolve at a higher level and might suggest a favorable response to immunotherapy. Our study also identified several potential driver genes and promising biomarkers for the prognosis and prediction of chemotherapy responses in NSCLC.

Characterization of T-Cell Epitopes in Food Allergens by Bioinformatic Tools

The identification of T-cell epitopes is a critical step in the understanding of the immunologic mechanisms such as food allergy. Epitope screening in silico by bioinformatic tools can be used to identify T-cell epitopes, which can save time and resources. In this chapter, a multiparametric approach to predict and assess major histocompatibility complex (MHC) class II binding T-cell epitopes using bioinformatics was introduced for food allergens. Furthermore, the ability of predicted T-cell epitopes to induce interleukin (IL)-4, as well as the allergenicity potential based on the sequence analysis and population coverage of epitopes were also determined. The molecular docking approach was further used to explore the binding ability between epitopes and human leukocyte antigen (HLA) class II molecules. The amino acids that might be responsible for binding to HLA class II molecules and their binding interactions were analyzed.

In-silico development of multi-epitope subunit vaccine against lymphatic filariasis

The World Health Organization in 2022 reported that more than 863 million people in 50 countries are at risk of developing lymphatic filariasis (LF), a disease caused by parasitic infection. Immune responses to parasites suggest that the development of a prophylactic vaccine against LF is possible. Using a reverse vaccinology approach, the current study identified Trehalose-6-phosphatase (TPP) as a potential vaccine candidate among 15 reported vaccine antigens for B. malayi. High-ranking B and T-cell epitopes in the Trehalose-6-phosphatase (TPP) were shortlisted using online servers for subsequent analysis. We selected these peptides to construct a vaccine model using I-TASSER and GalaxyRefine server. The vaccine construct showed favorable physicochemical properties, high antigenicity, no allergenicity, no toxicity, and high stability. Structural validation using the Ramachandran plot showed that 98% of the residues were in favorable or mostly allowed regions. Molecular docking and simulation showed a strong binding affinity and stability of the subunit vaccine with toll-like receptor 4 (TLR4). Furthermore, the subunit vaccine showed a strong IgG/IgM response, with the disappearance of the antigen. We propose that our vaccine construct should be further evaluated using cellular and animal models to develop a vaccine that is safe and effective against LF.Communicated by Ramaswamy H. Sarma.

In silico analysis of HLA-1 and HLA-2 recognition of a designed recombinant human papillomavirus vaccine based on L1 protein HPV subtype 45

BACKGROUND

Human leukocyte antigen (HLA) can bind and present the processed antigenic peptide derived from the vaccine to the T cell receptor, and this capability is crucial in determining the effectivity of the vaccine to terminate virus-infected cells, activate macrophages, and induce B cells to produce antibodies. A recombinant vaccine candidate based on protein L1 HPV45 was designed and analysed whether it is recognisable by T cells through the binding of their epitopes to HLAs.

METHODS

The study consisted of two parts: part one was the analysis of the L1 recombinant protein binding to HLA-1 and 2 epitopes, whereas part two was the distribution analysis of HPV-linked HLA allele. HLA allele sets found at high frequency in the general population and in specific Indonesian population were listed for the binding analysis of the recombinant L1 HPV45 protein. In part one, immunoepitope servers from IEDB were used to predict the binding of the designed proteins to HLA alleles. The prediction method for MHC-I binding prediction was the NetMHCpan EL 4.1 whilst for MHC-II binding prediction was the Consensus approach. Antigenicity analysis for each peptide was conducted using VaxiJen 2.0 with the threshold 1.0 to select the highly antigenic peptides, and positions of these epitopes in the secondary and tertiary structure of the recombinant protein were also predicted. The percent population coverage of the alleles capable of binding to these epitopes worldwide was also estimated. In part two, the worldwide distribution and frequency of HPV-related HLA-1 and 2 were studied.

RESULT

Two highly antigenic peptides (EEYDLQFIF and KLKFWTVDLK) were recognised by high-frequency HLA-1 alleles in both, the general and Western Javanese. In addition to these two epitopes, a few more peptides are also recognised by the high-frequency Western Javanese HLA-1 alleles, which are not in Weiskopf's list of high-frequency HLA-1 alleles in the general population. Analysis of the highly antigenic epitopes binding to HLA-DRB1 alleles in general (YIKGTSANM) and Western Javanese (LRRRPTIGP) populations showed that these peptide cores associate to HLA-DRB1*04, albeit the different sub-types, due to the presence of different allele in each population group. Analysis of the epitopes and the positive binding alleles showed on average 25.65% population coverage.

CONCLUSION

The recombinant vaccine candidate based on protein L1 HPV45 is presumed to contain highly antigenic peptides that can bind to high-frequency HLA-1 and 2 alleles present in general and Western Javanese populations. It was expected that the protein is capable of eliciting T cell-mediated responses in both populations; however, in vitro study is needed to prove the protectiveness of the designed recombinant protein.

Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges

Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.

Molecular Mimicry between SARS-CoV-2 Proteins and Human Self-Antigens Related with Autoimmune Central Nervous System (CNS) Disorders

SARS-CoV-2 can trigger autoimmune central nervous system (CNS) diseases in genetically susceptible individuals, a mechanism poorly understood. Molecular mimicry (MM) has been identified in other viral diseases as potential triggers of autoimmune CNS events. This study investigated if MM is the process through which SARS-CoV-2 induces the breakdown of immune tolerance. The frequency of autoimmune CNS disorders was evaluated in a prospective cohort with patients admitted to the COVID-19 Intense Care Unity (ICU) in Rio de Janeiro. Then, an in silico analysis was performed to identify the conserved regions that share a high identity between SARS-CoV-2 antigens and human proteins. The sequences with significant identity and antigenic properties were then assessed for their binding capacity to HLA subtypes. Of the 112 patients included, 3 were classified as having an autoimmune disorder. A total of eleven combinations had significant linear and three-dimensional overlap. NMDAR1, MOG, and MPO were the self-antigens with more significant combinations, followed by GAD65. All sequences presented at least one epitope with strong or intermediate binding capacity to the HLA subtypes selected. This study underscores the possibility that CNS autoimmune attacks observed in COVID-19 patients, including those in our population, could be driven by MM in genetically predisposed individuals.

Expression of mammalian cell entry genes in clinical isolates of M. tuberculosis and the cell entry potential and immunological reactivity of the Rv0590A protein

Mammalian cell entry (mce) operons play a vital role in cell invasion and survival of M. tuberculosis. Of the mce genes, the function of Rv0590A is still unknown. The present study was performed to investigate the function and immunogenic properties of the protein Rv0590A. Human leukemia monocytic cell line (THP-1) derived macrophages were infected with M. tuberculosis H37Rv at 3, 6, and 24 h of infection. The maximum colony forming units (CFU) were observed at 6 h (p < 0.005), followed by 3 h after infection. M. tuberculosis H37Rv and clinical isolates representative of Delhi/CAS, EAI, Beijing, Haarlem and Euro-American-superlineage were included in the study for expression analysis of mce1A, mce2A, mce3A, mce4A, and Rv0590A genes. Maximum upregulation of all mce genes was observed at 3 h of infection. All the five clinical isolates and H37Rv upregulated Rv0590A at various time points. Macrophage infection with M. tuberculosis H37Rv-overexpressing Rv0590A gene showed higher intracellular CFU as compared to that of wild-type H37Rv. Further, purified Rv0590A protein stimulated the production of TNFα, IFNγ, and IL-10 in macrophages. Thus, Rv0590A was found to be involved in cell invasion and showed good immunological response.

Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.

DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction

Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred .

Genomic profiling of subcutaneous patient-derived xenografts reveals immune constraints on tumor evolution in childhood solid cancer

Subcutaneous patient-derived xenografts (PDXs) are an important tool for childhood cancer research. Here, we describe a resource of 68 early passage PDXs established from 65 pediatric solid tumor patients. Through genomic profiling of paired PDXs and patient tumors (PTs), we observe low mutational similarity in about 30% of the PT/PDX pairs. Clonal analysis in these pairs show an aggressive PT minor subclone seeds the major clone in the PDX. We show evidence that this subclone is more immunogenic and is likely suppressed by immune responses in the PT. These results suggest interplay between intratumoral heterogeneity and antitumor immunity may underlie the genetic disparity between PTs and PDXs. We further show that PDXs generally recapitulate PTs in copy number and transcriptomic profiles. Finally, we report a gene fusion LRPAP1-PDGFRA. In summary, we report a childhood cancer PDX resource and our study highlights the role of immune constraints on tumor evolution.

In Silico Design of a New Epitope-Based Vaccine against Grass Group 1 Allergens

Allergic diseases are a global public health problem that affects up to 30% of the population in industrialized societies. More than 40% of allergic patients suffer from grass pollen allergy. Grass pollen allergens of group 1 and group 5 are the major allergens, since they induce allergic reactions in patients at high rates. In this study, we used immunoinformatic approaches to design an effective epitope-based vaccine against the grass group 1 allergens. After the alignment of all known pollen T-cell and B-cell epitopes from pollen allergens available in the public databases, the epitope GTKSEVEDVIPEGWKADTSY was identified as the most suitable for further analyses. The target sequence was subjected to immunoinformatics analyses to predict antigenic T-cell and B-cell epitopes. Population coverage analysis was performed for CD8+ and CD4+ T-cell epitopes. The selected T-cell epitopes (VEDVIPEGW and TKSEVEDVIPEGWKA) covered 78.87% and 98.20% of the global population and 84.57% and 99.86% of the population of Europe. Selected CD8+, CD4+ T-cell and B-cell epitopes have been validated by molecular docking analysis. CD8+ and CD4+ T-cell epitopes showed a very strong binding affinity to major histocompatibility complex (MHC) class I (MHC I) molecules and MHC class II (MHC II) molecules with global energy scores of -72.1 kcal/mol and -89.59 kcal/mol, respectively. The human IgE-Fc (PDB ID 4J4P) showed a lower affinity with B-cell epitope (ΔG = -34.4 kcal/mol), while the Phl p 2-specific human IgE Fab (PDB ID 2VXQ) had the lowest binding with the B-cell epitope (ΔG = -29.9 kcal/mol). Our immunoinformatics results demonstrated that the peptide GTKSEVEDVIPEGWKADTSY could stimulate the immune system and we performed ex vivo tests showed that the investigated epitope activates T cells isolated from patients with grass pollen allergy, but it is not recognized by IgE antibodies specific for grass pollen allergens. This confirms the importance of such studies to establish universal epitopes to serve as a basis for developing an effective vaccine against a particular group of allergens. Further in vivo studies are needed to validate the effectiveness of such a vaccine against grass pollen allergens.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

A genomic platform for surveillance and antigen discovery in Plasmodium spp. using long-read amplicon sequencing

Many vaccine candidate proteins in the malaria parasite Plasmodium falciparum are under strong immunological pressure and confer antigenic diversity. We present a sequencing and data analysis platform for the genomic surveillance of the insertion or deletion (indel)-rich antigens merozoite surface protein 1 (MSP1), MSP2, glutamate-rich protein (GLURP), and CSP from P. falciparum using long-read circular consensus sequencing (CCS) in multiclonal malaria isolates. Our platform uses 40 PCR primers per gene to asymmetrically barcode and identify multiclonal infections in pools of up to 384 samples. With msp2, we validated the method using 235 mock infections combining 10 synthetic variants at different concentrations and infection complexities. We applied this strategy to P. falciparum isolates from a longitudinal cohort in Tanzania. Finally, we constructed an analysis pipeline that streamlines the processing and interpretation of epidemiological and antigenic diversity data from demultiplexed FASTQ files. This platform can be easily adapted to other polymorphic antigens of interest in Plasmodium or any other human pathogen.

Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins

Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.

Novel "GaEl Antigenic Patches" Identified by a "Reverse Epitomics" Approach to Design Multipatch Vaccines against NIPAH Infection, a Silent Threat to Global Human Health

Nipah virus (NiV) is a zoonotic virus that causes lethal encephalitis and respiratory disease with the symptom of endothelial cell-cell fusion. Several NiV outbreaks have been reported since 1999 with nearly annual occurrences in Bangladesh. The outbreaks had high mortality rates ranging from 40 to 90%. No specific vaccine has yet been reported against NiV. Recently, several vaccine candidates and different designs of vaccines composed of epitopes against NiV were proposed. Most of the vaccines target single protein or protein complex subunits of the pathogen. The multiepitope vaccines proposed also cover a largely limited number of epitopes, and hence, their efficiency is still uncertain. To address the urgent need for a specific and effective vaccine against NiV infection, in the present study, we have utilized the "reverse epitomics" approach ("overlapping-epitope-clusters-to-patches" method) to identify "antigenic patches" (Ag-Patches) and utilize them as immunogenic composition for multipatch vaccine (MPV) design. The designed MPVs were analyzed for immunologically crucial parameters, physiochemical properties, and interaction with Toll-like receptor 3 ectodomain. In total, 30 CTL (cytotoxic T lymphocyte) and 27 HTL (helper T lymphocyte) antigenic patches were identified from the entire NiV proteome based on the clusters of overlapping epitopes. These identified Ag-Patches cover a total of discrete 362 CTL and 414 HTL epitopes from the entire proteome of NiV. The antigenic patches were utilized as immunogenic composition for the design of two CTL and two HTL multipatch vaccines. The 57 antigenic patches utilized here cover 776 overlapping epitopes targeting 52 different HLA class I and II alleles, providing a global ethnically distributed human population coverage of 99.71%. Such large number of epitope coverage resulting in large human population coverage cannot be reached with single-protein/subunit or multiepitope based vaccines. The reported antigenic patches also provide potential immunogenic composition for early detection diagnostic kits for NiV infection. Further, all the MPVs and Toll-like receptor ectodomain complexes show a stable nature of molecular interaction with numerous hydrogen bonds, salt bridges, and nonbounded contact formation and acceptable root mean square deviation and fluctuation. The cDNA analysis shows a favorable large-scale expression of the MPV constructs in a human cell line. By utilizing the novel "reverse epitomics" approach, highly immunogenic novel "GaEl antigenic patches" (GaEl Ag-Patches), a synonym term for "antigenic patches", were identified and utilized as immunogenic composition to design four MPVs against NiV. We conclude that the novel multipatch vaccines are potential candidates to combat NiV, with greater effectiveness, high specificity, and large human population coverage worldwide.

DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction

MOTIVATION

Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels.

RESULTS

The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction.

AVAILABILITY AND IMPLEMENTATION

DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

Screening of apical membrane antigen-1 (AMA1), dense granule protein-7 (GRA7) and rhoptry protein-16 (ROP16) antigens for a potential vaccine candidate against Toxoplasma gondii for chickens

Toxoplasmosis is a zoonotic disease caused by the protozoan parasite, Toxoplasma gondii known to infect almost all animals, including birds and humans globally. This disease has impacted the livestock industry and public health, where infection of domestic animals increases the zoonotic risk of transmission of infection to humans, threatening public health. Hence the need to discover novel and safe vaccines to fight against toxoplasmosis. In the current study, a novel multiepitope vaccine was designed using immunoinformatics techniques targeting T. gondii AMA1, GRA7 and ROP16 antigens, consisting of antigenic, immunogenic, non-allergenic and cytokine inducing T-cell (9 CD8⁺ and 15 CD4⁺) epitopes and four (4) B-cell epitopes fused together using AAY, KK and GPGPG linkers. The tertiary model of the proposed vaccine was predicted and validated to confirm the structural quality of the vaccine. The designed vaccine was highly antigenic (antigenicity = 0.6645), immunogenic (score = 2.89998), with molecular weight of 73.35 kDa, instability and aliphatic index of 28.70 and 64.10, respectively; and GRAVY of -0.363. The binding interaction, stability and flexibility were assessed with molecular docking and dynamics simulation, which revealed the proposed vaccine to have good structural interaction (binding affinity = -106.882 kcal/mol) and stability when docked with Toll like receptor-4 (TLR4). The results revealed that the Profilin-adjuvanted vaccine is promising, as it predicted induction of enhanced immune responses through the production of cytokines and antibodies critical in blocking host invasion.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

Multi-antigen spherical nucleic acid cancer vaccines

Cancer vaccines must activate multiple immune cell types to be effective against aggressive tumours. Here we report the impact of the structural presentation of two antigenic peptides on immune responses at the transcriptomic, cellular and organismal levels. We used spherical nucleic acid (SNA) nanoparticles to investigate how the spatial distribution and placement of two antigen classes affect antigen processing, cytokine production and the induction of memory. Compared with single-antigen SNAs, a single dual-antigen SNA elicited a 30% increase in antigen-specific T cell activation and a two-fold increase in T cell proliferation. Antigen placement within dual-antigen SNAs altered the gene expression of T cells and tumour growth. Specifically, dual-antigen SNAs encapsulating antigens targeting helper T cells and with externally conjugated antigens targeting cytotoxic T cells elevated antitumour genetic pathways, stalling lymphoma tumours in mice. Additionally, when combined with the checkpoint inhibitor anti-programmed-cell-death protein-1 in a mouse model of melanoma, a specific antigen arrangement within dual-antigen SNAs suppressed tumour growth and increased the levels of circulating memory T cells. The structural design of multi-antigen vaccines substantially impacts their efficacy.

A pilot study of lymphodepletion intensity for peripheral blood mononuclear cell-derived neoantigen-specific CD8 + T cell therapy in patients with advanced solid tumors

Currently, the optimal lymphodepletion intensity for peripheral blood mononuclear cell-derived neoantigen-specific CD8 + T cell (Neo-T) therapy has yet to be determined. We report a single-arm, open-label and non-randomized phase 1 study (NCT02959905) of Neo-T therapy with lymphodepletion at various dose intensity in patients with locally advanced or metastatic solid tumors that are refractory to standard therapies. The primary end point is safety and the secondary end points are disease control rate (DCR), progression-free survival (PFS), overall survival (OS). Results show that the treatment is well tolerated with lymphopenia being the most common adverse event in the highest-intensity lymphodepletion groups. Neo-T infusion-related adverse events are only grade 1-2 in the no lymphodepletion group. The median PFS is 7.1 months (95% CI:3.7-9.8), the median OS is 16.8 months (95% CI: 11.9-31.7), and the DCR is 66.7% (6/9) among all groups. Three patients achieve partial response, two of them are in the no lymphodepletion group. In the group without lymphodepletion pretreatment, one patient refractory to prior anti-PD1 therapy shows partial response to Neo-T therapy. Neoantigen specific TCRs are examined in two patients and show delayed expansion after lymphodepletion treatment. In summary, Neo-T therapy without lymphodepletion could be a safe and promising regimen for advanced solid tumors.

A protective, single-visit TB vaccination regimen by co-administration of a subunit vaccine with BCG

The only licensed tuberculosis (TB) vaccine, Bacillus Calmette Guerin (BCG), fails to reliably protect adolescents and adults from pulmonary TB, resulting in ~1.6 million deaths annually. Protein subunit vaccines have shown promise against TB in clinical studies. Unfortunately, most subunit vaccines require multiple administrations, which increases the risk of loss to follow-up and necessitates more complex and costly logistics. Given the well-documented adjuvant effect of BCG, we hypothesized that BCG co-administration could compensate for a reduced number of subunit vaccinations. To explore this, we developed an expression-optimized version of our H107 vaccine candidate (H107e), which does not cross-react with BCG. In the CAF®01 adjuvant, a single dose of H107e induced inferior protection compared to three H107e/CAF®01 administrations. However, co-administering a single dose of H107e/CAF®01 with BCG significantly improved protection, which was equal to BCG co-administered with three H107e/CAF®01 doses. Importantly, combining BCG with a single H107e/CAF®01 dose also increased protection in previously BCG-primed animals. Overall, a single dose of H107e/CAF®01 with BCG induced long-lived immunity and triggered BCG-specific Th17 responses. These data support co-administration of BCG and subunit vaccines in both BCG naïve and BCG-primed individuals as an improved TB vaccine strategy with reduced number of vaccination visits.

Design peptide and multi-epitope protein vaccine candidates against monkeypox virus using reverse vaccinology approach: an in-silico study

Monkeypox is a zoonotic virus that has recently affected different countries worldwide. On July 23, 2022, the WHO declared the outbreak of monkeypox as a public health emergency of international concern. Surveillance studies conducted in Central Africa in the 1980s and later during outbreaks in the same region showed smallpox vaccines to be clinically somewhat effective against Monkeypox virus. However, there is no specific vaccine against this virus. This research used bioinformatics techniques to establish a novel multi-epitope vaccine candidate against Monkeypox that can induce a strong immune response. Five well-known antigenic proteins (E8L, A30L, A35R, A29L, and B21R) of the virus were picked and assessed as possible immunogenic peptides. Two suitable peptide candidates were selected according to bio-informatics analysis. Based upon in silico evaluation, two multi-epitope vaccine candidates (ALALAR and ALAL) were built with rich-epitope domains consisting of high-ranking T and B-cell epitopes. After predicting and evaluating the 3D structure of the protein candidates, the most efficient 3D models were considered for docking studies with Toll-like receptor 4 (TLR4) and the HLA-A * 11:01, HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*07:02, HLA-A*15:01, HLA-A*30:01 receptors. Subsequently, molecular dynamics (MD) simulation of up to 150 nanoseconds was employed to assess the durability of the interaction of the vaccine candidates with immune receptors. MD studies showed that M5-HLA-A*11:01, ALAL-TLR4, and ALALAR-TLR4 complexes were stable during simulation. Analysis of the in silico outcomes indicates that the M5 peptide and ALAL and ALALAR proteins may be suitable vaccine candidates against the Monkeypox virus.Communicated by Ramaswamy H. Sarma.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome

Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

TransMHCII: a novel MHC-II binding prediction model built using a protein language model and an image classifier

The emergence of deep learning models such as AlphaFold2 has revolutionized the structure prediction of proteins. Nevertheless, much remains unexplored, especially on how we utilize structure models to predict biological properties. Herein, we present a method using features extracted from protein language models (PLMs) to predict the major histocompatibility complex class II (MHC-II) binding affinity of peptides. Specifically, we evaluated a novel transfer learning approach where the backbone of our model was interchanged with architectures designed for image classification tasks. Features extracted from several PLMs (ESM1b, ProtXLNet or ProtT5-XL-UniRef) were passed into image models (EfficientNet v2b0, EfficientNet v2m or ViT-16). The optimal pairing of the PLM and image classifier resulted in the final model TransMHCII, outperforming NetMHCIIpan 3.2 and NetMHCIIpan 4.0-BA on the receiver operating characteristic area under the curve, balanced accuracy and Jaccard scores. The architecture innovation may facilitate the development of other deep learning models for biological problems.

Mechanisms that promote the evolution of cross-reactive antibodies upon vaccination with designed influenza immunogens

Immunogens that elicit broadly neutralizing antibodies targeting the conserved receptor-binding site (RBS) on influenza hemagglutinin may serve as candidates for a universal influenza vaccine. Here, we develop a computational model to interrogate antibody evolution by affinity maturation after immunization with two types of immunogens: a heterotrimeric "chimera" hemagglutinin that is enriched for the RBS epitope relative to other B cell epitopes and a cocktail composed of three non-epitope-enriched homotrimers of the monomers that comprise the chimera. Experiments in mice find that the chimera outperforms the cocktail for eliciting RBS-directed antibodies. We show that this result follows from an interplay between how B cells engage these antigens and interact with diverse helper T cells and requires T cell-mediated selection of germinal center B cells to be a stringent constraint. Our results shed light on antibody evolution and highlight how immunogen design and T cells modulate vaccination outcomes.

Bioinformatics analysis of Omp19 and Omp25 proteins for designing multi-epitope vaccines against Brucella

Brucellosis is a zoonotic disease caused by Brucella. There is no effective vaccine against human brucellosis. Omp19 and Omp25 are the outer membrane proteins of Brucella. They are widely expressed and highly conserved in Brucella and have high immunogenicity. Herein, we aim to identify multi-epitope vaccine candidates based on Omp19 and Omp25. We analyzed the physicochemical properties and protein structure of Omp19 and Omp25, and predicted the corresponding B cell and T cell epitopes using bioinformatics analysis. Omp19 and Omp25 were composed of 177 amino acids and 213 amino acids, respectively. They were both stable hydrophilic proteins. The instability indices were 44.8 and 23, respectively. The hydrophilicity was -0.1 and -0.317, respectively. In the secondary structure of Omp19 and Omp25 proteins, the α-helix accounted for 12.43% and 23.94%, the β-sheet was 18.64% and 23.47%, the β-turn was 6.78% and 4.23%, and the random coil was 62.15% and 48.36%. Finally, 5 B cell epitopes, 3 Th-cell epitopes and 5 CTL cell epitopes of Omp19 protein, and 4 B cell epitopes, 3 Th-cell epitopes, and 5 CTL cell epitopes of Omp25 protein were selected as vaccine candidates. In conclusion, we obtained potential B cell and T cell epitopes of the Brucella outer membrane Omp19 and Omp25 proteins. This lays the foundation for the further design of multi-epitope vaccine of Brucella.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Molecular and immunological characterization of the calcyclin binding protein in rodent malaria parasite

Malaria remains as a global life-threatening disorder due to the emergence of resistance against standard antimalarials. Consequently, there is a serious need to better understand the biology of the malaria parasite in order to determine appropriate targets for new interventions. Calcyclin binding protein (CacyBP) is a multi-functional and multi-ligand protein that is not well characterized in malaria disease. In this study, we have cloned CacyBP from rodent species Plasmodium yoelii nigeriensis and purified the recombinant protein to carry out its detailed molecular, biophysical and immunological characterization. Molecular characterization indicates that PyCacyBP is a ∼27 kDa protein in parasite lysate and exists in monomer and dimer forms. Bioinformatic analysis of CacyBP showed significant sequence and structural similarities between rodent and human malaria parasites. CacyBP is expressed in all blood stages of P. yoelii nigeriensis parasite. In silico studies proposed the immunogenic potential of CacyBP. The rPyCacyBP immunized mice exhibited elevated levels of IgG1, IgG2a, IgG2b and IgG3 in their serum. Notably, cellular immune response in splenocytes from immunized mice showed increased expression of pro-inflammatory cytokines such as IL-12, IFN-γ and TNF-α. This CacyBP exhibited pro-inflammatory immune response in rodent host. These finding revealed that CacyBP may have the potential to boost the host immunity for protection against malaria infection. The present study provides basis for further exploration of the biological function of CacyBP in malaria parasite.

Design and construction of multi epitope- peptide vaccine candidate for rabies virus

Rabies virus is a zoonotic pathogen that causes lethal encephalitis with a case fatality rate of almost 100% in unvaccinated individuals. The currently available vaccines against rabies are composed of inactivated viral particles that only confer a short-term immune response. It is well-known that the entry of rabies virus into host cells is mediated by a trimeric glycoprotein presents on the surface of viral envelope. As the sole viral surface protein, this trimeric glycoprotein represents a promising molecular target to design new vaccines and neutralizing antibodies against rabies virus. Epitope mapping studies had identified several antigenic sites on the surface of trimeric pre-fusion glycoprotein of rabies virus. Therefore, it is of interest to screen the rabies virus glycoprotein by different web-based immuno-informatics tools to identify potential B-cells and T-cells linear epitopes. Here, we present a construct of peptide vaccine that consists of these predicted linear epitopes of rabies virus glycoprotein together with appropriate linkers and adjuvant. Various online prediction tools, molecular docking and dynamics simulation assume that the vaccine construct may be stable, safe and effective. However, validation of these in-silico results is necessary both in vitro and in vivo setting.

Immunoinformatics design of multi-epitope vaccine using OmpA, OmpD and enterotoxin against non-typhoidal salmonellosis

BACKGROUND

Non-typhoidal Salmonella (NTS) is one of the important bacteria that cause foodborne diseases and invasive infections in children and elderly people. Since NTS infection is difficult to control due to the emergence of antibiotic-resistant species and its adverse effect on immune response, the development of a vaccine against NTS would be necessary. This study aimed to develop a multi-epitope vaccine against the most prevalent serovars of NTS (Salmonella Typhimurium, Salmonella Enteritidis) using an immunoinformatics approach and targeting OmpA, OmpD, and enterotoxin (Stn).

RESULTS

Initially, the B cell and T cell epitopes were predicted. Then, epitopes and suitable adjuvant were assembled by molecular linkers to construct a multi-epitope vaccine. The computational tools predicted the tertiary structure, refined the tertiary structure and validated the final vaccine construct. The effectiveness of the vaccine was evaluated via molecular docking, molecular dynamics simulation, and in silico immune simulation. The vaccine model had good binding affinity and stability with MHC-I, MHC-II, and toll-like receptors (TLR-1, 2, 4) as well as activation of T cells, IgM, IgG, IFN-γ and IL-2 responses. Furthermore, after codon optimization of the vaccine sequence, this sequence was cloned in E. coli plasmid vector pET-30a (+) within restriction sites of HindIII and BamHI.

CONCLUSIONS

This study, for the first time, introduced a multi-epitope vaccine based on OmpA, OmpD and enterotoxin (Stn) of NTS that could stimulate T and B cell immune responses and produced in the prokaryotic system. This vaccine was validated in-silico phase which is an essential study to reduce challenges before in vitro and in vivo studies.