The Immune Epitope Database: How Data Are Entered and Retrieved

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

Advancements and Challenges in Peptide-Based Cancer Vaccination: A Multidisciplinary Perspective

With the continuous advancements in tumor immunotherapy, researchers are actively exploring new treatment methods. Peptide therapeutic cancer vaccines have garnered significant attention for their potential in improving patient outcomes. Despite its potential, only a single peptide-based cancer vaccine has been approved by the U.S. Food and Drug Administration (FDA). A comprehensive understanding of the underlying mechanisms and current development status is crucial for advancing these vaccines. This review provides an in-depth analysis of the production principles and therapeutic mechanisms of peptide-based cancer vaccines, highlights the commonly used peptide-based cancer vaccines, and examines the synergistic effects of combining these vaccines with immunotherapy, targeted therapy, radiotherapy, and chemotherapy. While some studies have yielded suboptimal results, the potential of combination therapies remains substantial. Additionally, we addressed the management and adverse events associated with peptide-based cancer vaccines, noting their relatively higher safety profile compared to traditional radiotherapy and chemotherapy. Lastly, we also discussed the roles of adjuvants and targeted delivery systems in enhancing vaccine efficacy. In conclusion, this review comprehensively outlines the current landscape of peptide-based cancer vaccination and underscores its potential as a pivotal immunotherapy approach.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Developability considerations for bispecific and multispecific antibodies

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine's design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of -1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.Communicated by Ramaswamy H. Sarma.

Potential and limitations of epitope mapping and molecular targeting in Hymenoptera venom allergy

Hymenoptera venom (HV) allergy can lead to life threatening conditions by specific IgE (sIgE)-mediated anaphylactic reactions. The knowledge about major allergens from venom of different clinically relevant species increased in the last decades, allowing the development of component-resolved diagnostics in which sIgE to single allergens is analysed. Despite these advances, the precise regions of the allergens that bind to IgE are only known for few HV allergens. The detailed characterization of IgE epitopes may provide valuable information to improve immunodiagnostic tests and to develop new therapeutic strategies using allergen-derived peptides or other targeted approaches. Epitope-resolved analysis is challenging, since the identification of conformational epitopes present in many allergens demands complex technologies for molecular analyses. Furthermore, functional analysis of the epitopeś interaction with their respective ligands is needed to distinguish epitopes that can activate the allergic immune response, from those that are recognized by irrelevant antibodies or T cell receptors from non-effector cells. In this review, we focus on the use of mapping and molecular targeting approaches for characterization of the epitopes of the major venom allergens of clinically relevant Hymenoptera species. The screening of the most relevant allergen peptides by epitope mapping could be helpful for the development of molecules that target major and immunodominant epitopes blocking the allergen induced cellular reactions as novel approach for the treatment of HV allergy.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

A meta-analysis of epitopes in prostate-specific antigens identifies opportunities and knowledge gaps

BACKGROUND

The Cancer Epitope Database and Analysis Resource (CEDAR) is a newly developed repository of cancer epitope data from peer-reviewed publications, which includes epitope-specific T cell, antibody, and MHC ligand assays. Here we focus on prostate cancer as our first cancer category to demonstrate the capabilities of CEDAR, and to shed light on the advances of epitope-related prostate cancer research.

RESULTS

The meta-analysis focused on a subset of data describing epitopes from 8 prostate-specific (PS) antigens. A total of 460 epitopes were associated with these proteins, 187 T cell, 109B cell, and 271 MHC ligand epitopes. The number of epitopes was not correlated with the length of the protein; however, we found a significant positive correlation between the number of references per specific PS antigen and the number of reported epitopes. Forty-four different class I and 27 class II restrictions were found, with the most epitopes described for HLA-A*02:01 and HLA-DRB1*01:01. Cytokine assays were mostly limited to IFNg assays and a very limited number of tetramer assays were performed. Monoclonal and polyclonal B cell responses were balanced, with the highest number of epitopes studied in ELISA/Western blot assays. Additionally, epitopes were generically described as associated with prostate cancer, with little granularity specifying diseases state. We found that in vivo and tumor recognition assays were sparse, and the number of epitopes with annotated B/T cell receptor information were limited. Potential immunodominant regions were identified by the use of the ImmunomeBrowser tool.

CONCLUSION

CEDAR provides a comprehensive repository of epitopes related to prostate-specific antigens. This inventory of epitope data with its wealth of searchable T cell, B cell and MHC ligand information provides a useful tool for the scientific community. At the same time, we identify significant knowledge gaps that could be addressed by experimental analysis.

Refining the DC-targeting vaccination for preventing emerging infectious diseases

The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.

Self-mediated positive selection of T cells sets an obstacle to the recognition of nonself

Adaptive immune recognition is mediated by the binding of peptide-human leukocyte antigen complexes by T cells. Positive selection of T cells in the thymus is a fundamental step in the generation of a responding T cell repertoire: only those T cells survive that recognize human peptides presented on the surface of cortical thymic epithelial cells. We propose that while this step is essential for optimal immune function, the process results in a defective T cell repertoire because it is mediated by self-peptides. To test our hypothesis, we focused on amino acid motifs of peptides in contact with T cell receptors. We found that motifs rarely or not found in the human proteome are unlikely to be recognized by the immune system just like the ones that are not expressed in cortical thymic epithelial cells or not presented on their surface. Peptides carrying such motifs were especially dissimilar to human proteins. Importantly, we present our main findings on two independent T cell activation datasets and directly demonstrate the absence of naïve T cells in the repertoire of healthy individuals. We also show that T cell cross-reactivity is unable to compensate for the absence of positively selected T cells. Additionally, we show that the proposed mechanism could influence the risk for different infectious diseases. In sum, our results suggest a side effect of T cell positive selection, which could explain the nonresponsiveness to many nonself peptides and could improve the understanding of adaptive immune recognition.

Molecularly Imprinted Polymer Nanogels for Protein Recognition: Direct Proof of Specific Binding Sites by Solution STD and WaterLOGSY NMR Spectroscopies

Molecularly imprinted polymers (MIPs) are tailor-made synthetic antibodies possessing specific binding cavities designed for a target molecule. Currently, MIPs for protein targets are synthesized by imprinting a short surface-exposed fragment of the protein, called epitope or antigenic determinant. However, finding the epitope par excellence that will yield a peptide "synthetic antibody" cross-reacting exclusively with the protein from which it is derived, is not easy. We propose a computer-based rational approach to unambiguously identify the "best" epitope candidate. Then, using Saturation Transfer Difference (STD) and WaterLOGSY NMR spectroscopies, we prove the existence of specific binding sites created by the imprinting of this peptide epitope in the MIP nanogel. The optimized MIP nanogel could bind the epitope and cognate protein with a high affinity and selectivity. The study was performed on Hepatitis A Virus Cell Receptor-1 protein, also known as KIM-1 and TIM-1, for its ubiquitous implication in numerous pathologies.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.

SARS-CoV-2 Infection and Guillain-Barré Syndrome

Severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) is a beta-coronavirus that emerged as a global threat and caused a pandemic following its first outbreak in Wuhan, China, in late 2019. SARS-CoV-2 causes COVID-19, a disease ranging from relatively mild to severe illness. Older people and those with many serious underlying medical conditions such as diabetes, heart or lung conditions are at higher risk for developing severe complications from COVID-19 illness. SARS-CoV-2 infections of adults can lead to neurological complications ranging from headaches, loss of taste and smell, to Guillain-Barré syndrome, an autoimmune disease characterized by neurological deficits. Herein we attempt to describe the neurological manifestations of SARS-CoV2 infection with a special focus on Guillain-Barré syndrome.

SARS-CoV-2 human T cell epitopes: Adaptive immune response against COVID-19

Over the past year, numerous studies in the peer reviewed and preprint literature have reported on the virological, epidemiological and clinical characteristics of the coronavirus, SARS-CoV-2. To date, 25 studies have investigated and identified SARS-CoV-2-derived T cell epitopes in humans. Here, we review these recent studies, how they were performed, and their findings. We review how epitopes identified throughout the SARS-CoV2 proteome reveal significant correlation between number of epitopes defined and size of the antigen provenance. We also report additional analysis of SARS-CoV-2 human CD4 and CD8 T cell epitope data compiled from these studies, identifying 1,400 different reported SARS-CoV-2 epitopes and revealing discrete immunodominant regions of the virus and epitopes that are more prevalently recognized. This remarkable breadth of epitope repertoire has implications for vaccine design, cross-reactivity, and immune escape by SARS-CoV-2 variants.

Convolutional neural networks with image representation of amino acid sequences for protein function prediction

Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. In this paper, we propose a convolutional neural network based approach ProtConv to predict the functionality of proteins by converting the amino-acid sequences to a two dimensional image. We have used a protein embedding technique using transfer learning to generate the feature vector. Feature vector is then converted into a square sized single channel image to be fed into a convolutional network. The neural network architecture used here is a combination of convolutional filters and average pooling layers followed by dense fully connected layers to predict a binary function. We have performed experiments on standard benchmark datasets taken from two very important protein function prediction task: proinflammatory cytokines and anticancer peptides. Our experiments show that the proposed method, ProtConv achieves state-of-the-art performances on both of the datasets. All necessary details about implementation with source code and datasets are made available at: https://github.com/swakkhar/ProtConv.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

A behind-the-scenes tour of the IEDB curation process: an optimized process empirically integrating automation and human curation efforts

The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B‐cell and T‐cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality‐checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea‐sharing among players in this ever‐growing space.

The Immune Epitope Database and Analysis Resource Program 2003-2018: reflections and outlook

The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.

Immunological considerations in the development of Pseudomonas aeruginosa vaccines

Pseudomonas aeruginosa is an opportunistic human pathogen capable of causing a wide range of potentially life-threatening infections. With multidrug-resistant P. aeruginosa infections on the rise, the need for a rationally-designed vaccine against this pathogen is critical. A number of vaccine platforms have shown promising results in pre-clinical studies, but no vaccine has successfully advanced to licensure. Growing evidence suggests that an effective P. aeruginosa vaccine may require Th17-type CD4⁺ T cells to prevent infection. In this review, we summarize recent pre-clinical studies of P. aeruginosa vaccines, specifically focusing on those that induce Th17-type cellular immunity. We also highlight the importance of adjuvant selection and immunization route in vaccine design in order to target vaccine-induced immunity to infected tissues. Advances in cellular immunology and adjuvant biology may ultimately influence better P. aeruginosa vaccine platforms that can protect targeted human populations.

Nucleoside Hydrolase NH 36: A Vital Enzyme for the Leishmania Genus in the Development of T-Cell Epitope Cross-Protective Vaccines

NH36 is a vital enzyme of the DNA metabolism and a specific target for anti-Leishmania chemotherapy. We developed second-generation vaccines composed of the FML complex or its main native antigen, the NH36 nucleoside hydrolase of Leishmania (L.) donovani and saponin, and a DNA vaccine containing the NH36 gene. All these vaccines were effective in prophylaxis and treatment of mice and dog visceral leishmaniasis (VL). The FML-saponin vaccine became the first licensed veterinary vaccine against leishmaniasis (Leishmune®) which reduced the incidence of human and canine VL in endemic areas. The NH36, DNA or recombinant protein vaccines induced a Th1 CD4+IFN-γ+ mediated protection in mice. Efficacy against VL was mediated by a CD4+TNF-α T lymphocyte response against the NH36-F3 domain, while against tegumentary leishmaniasis (TL) a CD8+ T lymphocyte response to F1 was also required. These domains were 36-41 % more protective than NH36, and a recombinant F1F3 chimera was 21% stronger than the domains, promoting a 99.8% reduction of the parasite load. We also identified the most immunogenic NH36 domains and epitopes for PBMC of active human VL, cured or asymptomatic and DTH+ patients. Currently, the NH36 subunit recombinant vaccine is turning into a multi-epitope T cell synthetic vaccine against VL and TL.

Vaccine Target Discovery

With the rise in novel infectious agents and disease pandemics, a new era of vaccine discovery is necessary. To address this, the new field of immunomics is described, which is synergistically powered by integrating bioinformatics methodologies with technological advances in biology and high-throughput instrumentation. By incorporating biological data from immunology and molecular biology with current genomics and proteomics, immunomics is geared to deliver an insight into immune function, optimal stimulation of immune responses and precise mapping and rational selection of immune targets that cover antigenic diversity. These efforts are expected to contribute towards the development of new generation of vaccines, tailored to both the genetic make-up of the human population and of the pathogen. Vaccine technologies are also being explored for prevention or control of non-communicable diseases.

Evaluating natural killer cell cytotoxicity against solid tumors using a microfluidic model

Immunotherapies against solid tumors face additional challenges compared with hematological cancers. In solid tumors, immune cells and antibodies need to extravasate from vasculature, find the tumor, and migrate through a dense mass of cells. These multiple steps pose significant obstacles for solid tumor immunotherapy and their study has remained difficult using classic in vitro models based on Petri dishes. In this work, a microfluidic model has been developed to study natural killer cell response. The model includes a 3D breast cancer spheroid in a 3D extracellular matrix, and two flanking lumens lined with endothelial cells, replicating key structures and components during the immune response. Natural Killer cells and antibodies targeting the tumor cells were either embedded in the matrix or perfused through the lateral blood vessels. Antibodies that were perfused through the lateral lumens extravasated out of the blood vessels and rapidly diffused through the matrix. However, tumor cell-cell junctions hindered antibody penetration within the spheroid. On the other hand, natural killer cells were able to detect the presence of the tumor spheroid several hundreds of microns away and penetrate the spheroid faster than the antibodies. Once inside the spheroid, natural killer cells were able to destroy tumor cells at the spheroid periphery and, importantly, also at the innermost layers. Finally, the combination of antibody-cytokine conjugates and natural killer cells led to an enhanced cytotoxicity located mostly at the spheroid periphery. Overall, these results demonstrate the utility of the model for informing immunotherapy of solid tumors.

Interleukin-22, a potent target for treatment of non-autoimmune diseases

Interleukin -22 (IL-22) is a member of interleukin-10 (IL-10) family cytokines that is produced by different types of lymphocytes included in both innate and adaptive immune systems. These lymphocytes include activated T cells, most notably Th17 and Th22 cells, as well as NK cells, γδ T cells, etc. IL-22 mediate its effects via the IL-22-IL-22R complex and subsequent Janus Kinase-signal transduces and activators transcription (JAK-STAT) signaling pathway. According to recent evidence, IL-22 played a critical role in the pathogenesis of many non-autoimmune diseases. In this review, we mainly discussed the recent findings and advancements of the role of IL-22 in several non-autoimmune diseases, such as acute lung injury, atherosclerosis and some bacterial infections, suggesting that IL-22 may have therapeutic potential for treating non-autoimmune diseases.

Analysis of the Association of Polymorphisms rs5743708 in TLR2 and rs4986790 in TLR4 with Atopic Dermatitis Risk

BACKGROUND

We carried out a meta-analysis to assess whether Toll-like receptor 2 (TLR2) rs5743708 and Toll-like receptor 4 (TLR4) rs4986790 polymorphisms are associated with the risk of atopic dermatitis.

METHODS

A systematic search of PubMed, Embase, and Web of Science was performed to identify eligible case-control studies on the association of rs5743708 and rs4986790 with the risk of atopic dermatitis. Statistical analyses of the odds ratio (OR), 95% confidence interval (CI), and p value were performed using STATA software.

RESULTS

Our meta-analysis included a total of nine case-control studies, all involving Caucasian populations. With respect to the TLR2 rs5743708 G/A polymorphism, there was a statistically significant difference in the overall risk of atopic dermatitis between the case and control groups [OR = 2.07, p value of association test, p_{(association)} = 0.001 in allele (A vs. G) model; OR = 1.93, p_{(association)} = 0.004 in carrier (A vs. G) model; OR = 2.07, p_{(association)} = 0.001 in heterozygote (GA vs. GG) model; OR = 1.99, p_{(association)} = 0.001 in dominant (GA+ AA vs. GG) model]. Similar positive results were observed in the subgroup analysis of "population-based control." For the TLR4 rs4986790 A/G polymorphism, an increased atopic dermatitis risk was detected in the case group under the allele [OR = 1.78, p_{(association)} = 0.013], carrier [OR = 1.69, p_{(association)} = 0.027] and heterozygote [OR = 1.74, p_{(association)} = 0.020] models, but not the dominant [OR = 1.44, p_{(association)} = 0.070] model, in comparison to the population-based control group.

CONCLUSION

Our meta-analysis revealed a novel finding that the heterogeneous "GA" genotype of the TLR2 rs5743708 and "AG" genotype of the TLR4 rs4986790 may be associated with increased susceptibility to atopic dermatitis in Caucasians.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

Deciphering the MHC-associated peptidome: a review of naturally processed ligand data

INTRODUCTION

The availability of big data sets ('OMICS') has greatly impacted fundamental and translational science. High-throughput analysis of HLA class I and II associated peptidomes by mass spectrometry (MS) has generated large datasets, with the last decade witnessing tremendous growth in the breadth and number of studies. Areas covered: For this, we first analyzed naturally processed peptide (NP) data captured within the IEDB to survey and characterize the current state of NP data. We next asked to what extent the NP data overlap with existing T cell epitope and MHC binding data. Expert commentary: The current collection of NP data represents a large and diverse set of class I/II peptides mostly derived from self-antigens. These data overlap only marginally with existing immunogenicity and binding data and it is thus difficult to ascertain the correspondence between the different assay methodologies. This highlights a need for unbiased studies benchmarking in model antigen systems how well MHC binding and NP data predicts immunogenicity. Going forward, efforts at generating an integrated process for capturing all NP, curating associated metadata and accessing NP data from an immunological viewpoint will be important for development of novel methods for identifying optimal target antigens and for class I and II epitope prediction.