Search

Purpose: Report relevance of molecular groups to clinicopathologic features, germline SMARCB1/SMARCA4 alterations (GLA), and survival of children with atypical teratoid rhabdoid tumor (ATRT) treated in two multi-institutional clinical trials. Materials and Methods: Seventy-four participants with newly diagnosed ATRT were treated in two trials: infants (SJYC07: age < 3 years; n = 52) and children (SJMB03: age 3–21 years; n = 22), using surgery, conventional chemotherapy (infants), or dose-dense chemotherapy with autologous stem cell rescue (children), and age- and risk-adapted radiotherapy [focal (infants) and craniospinal (CSI; children)]. Molecular groups ATRT-MYC (MYC), ATRT-SHH (SHH), and ATRT-TYR (TYR) were determined from tumor DNA methylation profiles. Results: Twenty-four participants (32%) were alive at time of analysis at a median follow-up of 8.4 years (range, 3.1–14.1 years). Methylation profiling classified 64 ATRTs as TYR (n = 21), SHH (n = 30), and MYC (n = 13), SHH group being associated with metastatic disease. Among infants, TYR group had the best overall survival (OS; P = 0.02). However, outcomes did not differ by molecular groups among infants with nonmetastatic (M0) disease. Children with M0 disease and Conclusions: Among infants, those with ATRT-TYR had the best OS. ATRT-SHH was associated with metastases and consequently with inferior outcomes. Children with nonmetastatic ATRT benefit from postoperative CSI and adjuvant chemotherapy.

Genomics has become indispensable for research in the last two decades. Completed and ongoing genome projects such as the UK Biobank, ICGC, TCGA and GenomeAsia100K have helped to understand several life-threatening diseases like cancer. Such initiatives from different countries have offered genomics-based diagnostics along with glues for therapies towards personalized healthcare. The Indian Agencies has started initiatives to catalogue the genome sequences of 20,000 individuals. The Department of Biotechnology (DBT) along with other scientific agencies has plans to sequence 10,000 healthy individuals and 10,000 diseased individuals. The Council of Scientific and Industrial Research (CSIR) also developed the "IndiGen" genome project where genome sequences for 1008 individuals are made available in Phase I. This will enable the development of a genome catalogue to introduce novel genomics-based clinical applications in future healthcare plan.

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

Ocular surface inflammatory disorder (OSID) is a spectrum of disorders that have features of several etiologies whilst displaying similar phenotypic signs of ocular inflammation. They are complicated disorders with underlying mechanisms related to several autoimmune disorders, such as rheumatoid arthritis (RA), Sj&ouml, gren&rsquo, s syndrome, and systemic lupus erythematosus (SLE). Current literature shows the involvement of both innate and adaptive arms of the immune system in ocular surface inflammation. The ocular surface contains distinct components of the immune system in the conjunctiva and the cornea. The normal conjunctiva epithelium and sub-epithelial stroma contains resident immune cells, such as T cells, B cells (adaptive), dendritic cells, and macrophages (innate). The relative sterile environment of the cornea is achieved by the tolerogenic properties of dendritic cells in the conjunctiva, the presence of regulatory lymphocytes, and the existence of soluble immunosuppressive factors, such as the transforming growth factor (TGF)-&beta, and macrophage migration inhibitory factors. With the presence of both innate and adaptive immune system components, it is intriguing to investigate the most important leukocyte population in the ocular surface, which is involved in immune surveillance. Our meta-analysis investigates into this with a focus on both infectious (contact lens wear, corneal graft rejection, Cytomegalovirus, keratitis, scleritis, ocular surgery) and non-infectious (dry eye disease, glaucoma, graft-vs-host disease, Sj&ouml, s syndrome) situations. We have found the predominance of dendritic cells in ocular surface diseases, along with the Th-related cytokines. Our goal is to improve the knowledge of immune cells in OSID and to open new dimensions in the field. The purpose of this study is not to limit ourselves in the ocular system, but to investigate the importance of dendritic cells in the disorders of other mucosal organs (e.g., lungs, gut, uterus). Holistically, we want to investigate if this is a common trend in the initiation of any disease related to the mucosal organs and find a unified therapeutic approach. In addition, we want to show the power of computational approaches to foster a collaboration between computational and biological science.

Summary Epitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the ‘clique method’, in which all members of the cluster must share the same minimal level of identity with each other, and the ‘connected graph method’, in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel ‘cluster‐breaking algorithm’ for consensus sequence driven sub‐clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the ‘clique method’, (ii) sets of allergen‐derived peptides from related species for the ‘connected graph method’ and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T‐cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed ‘cluster‐breaking algorithm’. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/.

Crowdsourcing has been used to address computational challenges in systems biology and assess translation of findings across species. Sub-challenge 2 of the sbv IMPROVER Systems Toxicology Challenge was designed to determine whether a common set of genes can be used to identify exposure to cigarette smoke in both human and mouse. Participating teams used a training set of human and mouse blood gene expression data to derive parsimonious models (up to 40 genes) that classify subjects into exposure groups: smokers, former smokers, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. Prediction of current exposure to cigarette smoke in human and mouse by a common prediction model was achieved by the top ranked team (Team 219) with 89% balanced accuracy (BAC), while past exposure was predicted with only 57% BAC. The prediction model of the top ranked team was a random forest classifier trained on sets of genes that appeared best for each species separately with no overlap between species. By contrast, Team 264, ranked second (tied with Team 250), selected genes that were simultaneously predictive in both species and achieved 80% and 59% BAC when predicting current and past exposure, respectively. These performance values were lower than the 96.5% and 61% BAC estimates for current and past exposure, respectively, obtained by Team 264 (top ranked in sub-challenge 1) when using only human data. Unlike past exposure, current exposure to cigarette smoke can be accurately assessed in both human and mouse with a common prediction model based on blood mRNAs. However, requiring a common gene signature to be predictive in both species resulted in a substantial decrease in balanced accuracy for prediction of current exposure to cigarette smoke (from 96.5% to 80%), suggesting species-specific responses exist.

Unwanted immune responses against protein therapeutics can reduce efficacy or lead to adverse reactions. T-cell responses are key in the development of such responses, and are directed against immunodominant regions within the protein sequence, often associated with binding to several allelic variants of HLA class II molecules (promiscuous binders). Herein, we report a novel computational strategy to predict 'de-immunized' peptides, based on previous studies of erythropoietin protein immunogenicity. This algorithm (or method) first predicts promiscuous binding regions within the target protein sequence and then identifies residue substitutions predicted to reduce HLA binding. Further, this method anticipates the effect of any given substitution on flanking peptides, thereby circumventing the creation of nascent HLA-binding regions. As a proof-of-principle, the algorithm was applied to Vatreptacog α, an engineered Factor VII molecule associated with unintended immunogenicity. The algorithm correctly predicted the two immunogenic peptides containing the engineered residues. As a further validation, we selected and evaluated the immunogenicity of seven substitutions predicted to simultaneously reduce HLA binding for both peptides, five control substitutions with no predicted reduction in HLA-binding capacity, and additional flanking region controls. In vitro immunogenicity was detected in 21·4% of the cultures of peptides predicted to have reduced HLA binding and 11·4% of the flanking regions, compared with 46% for the cultures of the peptides predicted to be immunogenic. This method has been implemented as an interactive application, freely available online at http://tools.iedb.org/deimmunization/.

Members of the flavivirus genus share a high level of sequence similarity and often circulate in the same geographical regions. However, whether T cells induced by one viral species cross-react with other related flaviviruses has not been globally addressed. Here, we tested pools of epitopes derived from dengue (DENV), zika (ZIKV), Japanese Encephalitis (JEV), West Nile (WNV), and yellow fever (YFV) viruses by Intracellular Cytokine Staining (ICS) using PBMCs of individuals naturally exposed to DENV or immunized with DENV (TV005) or YF17D vaccines. CD8 T cell responses recognized epitopes from multiple flaviviruses, however, the magnitude of cross-reactive responses was consistently several-fold lower than those to the autologous epitope pools, and associated with lower expression of activation markers such as CD40L, CD69, and CD137. Next, we characterized the antigen sensitivity of short-term T cell lines (TCL) representing twenty-nine different individual epitope/donor combinations. TCL derived from DENV monovalent vaccinees induced CD8 and CD4 T cells that cross-reacted within the DENV serocomplex but were consistently associated with more than 100-fold lower antigen sensitivity for most other flaviviruses, with no cross-recognition of YFV derived peptides. CD8 and CD4 TCL from YF17D vaccinees were associated with very limited cross-reactivity with any other flaviviruses, and in five out of eight cases more than 1000-fold lower antigen sensitivity. Overall, our data suggest limited cross-reactivity for both CD4 and CD8 T cell responses between flaviviruses and has implications for understanding immunity elicited by natural infection, and strategies to develop live attenuated vaccines against flaviviral species.ImportanceThe envelope (E) protein is the dominant target of neutralizing antibodies for dengue virus (DENV) and yellow fever virus (YFV). Accordingly, several DENV vaccine constructs use the E protein in a live attenuated vaccine format, utilizing a backbone derived from a heterologous flavivirus (such as YF) as a delivery vector. This backbone comprises the non-structural (NS) and capsid (C) antigens which are dominant targets of T cell responses. Here, we demonstrate that cross-reactivity at the level of T cell responses amongst different flaviviruses is very limited, despite high levels of sequence homology. Thus, the use of heterologous flavivirus species as a live attenuated vaccine vector is not likely to generate optimal T cell responses, and might thus impair vaccine performance.

The immune response elicited by the protective whole-cell pertussis (wP) versus the less-protective acellular pertussis (aP) vaccine has been well characterized; however, important clinical problems remain unsolved, as the inability of the currently administered aP vaccine is resulting in the reemergence of clinical disease (i.e., whooping cough). Strong evidence has shown that original, childhood aP and wP priming vaccines provide a long-lasting imprint on the CD4+ T cells that impacts protective immunity. However, aP vaccination might prevent disease but not infection, which might also affect the breadth of responses to Bordetella pertussis (BP) antigens. Thus, characterizing and defining novel targets associated with T cell reactivity are of considerable interest. Here, we compare the T cell reactivity of original aP and wP priming for different antigens contained or not contained in the aP vaccine and define the basis of a full-scale genomic map of memory T cell reactivity to BP antigens in humans. Our data show that the original priming after birth with aP vaccines has higher T cell reactivity than originally expected against a variety of BP antigens and that the genome-wide mapping of BP using an ex vivo screening methodology is feasible, unbiased, and reproducible. This could provide invaluable knowledge towards the direction of a new and improved pertussis vaccine design.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis. Fil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Yan, Zhen. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Kim, Haeuk. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca Fil: Jurtz, Vanessa. Technical University of Denmark; Dinamarca Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarca Fil: Greenbaum, Jason A. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca Fil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California; Estados Unidos Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina Fil: Peters, Bjoern. University of California; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos

In the present review, we summarize work from our as well as other groups related to the characterization of bacterial T cell epitopes, with a specific focus on two important pathogens, namely, Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), and Bordetella pertussis (BP), the bacterium that causes whooping cough. Both bacteria and their associated diseases are of large societal significance. Although vaccines exist for both pathogens, their efficacy is incomplete. It is widely thought that defects and/or alteration in T cell compartments are associated with limited vaccine effectiveness. As discussed below, a full genome-wide map was performed in the case of Mtb. For BP, our focus has thus far been on the antigens contained in the acellular vaccine; a full genome-wide screen is in the planning stage. Nevertheless, the sum-total of the results in the two different bacterial systems allows us to exemplify approaches and techniques that we believe are generally applicable to the mapping and characterization of human immune responses to bacterial pathogens. Finally, we add, as a disclaimer, that this review by design is focused on the work produced by our laboratory as an illustration of approaches to the study of T cell responses to Mtb and BP, and is not meant to be comprehensive, nor to detract from the excellent work performed by many other groups.

The Immune Epitope Database (IEDB, iedb.org) captures experimental data confined in figures, text and tables of the scientific literature, making it freely available and easily searchable to the public. The scope of the IEDB extends across immune epitope data related to all species studied and includes antibody, T cell, and MHC binding contexts associated with infectious, allergic, autoimmune, and transplant related diseases. Having been publicly accessible for >10 years, the recent focus of the IEDB has been improved query and reporting functionality to meet the needs of our users to access and summarize data that continues to grow in quantity and complexity. Here we present an update on our current efforts and future goals.

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes. Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Karosiene, Edita. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Jurtz, Vanessa. Technical University of Denmark; Dinamarca Fil: Edwards, Lindy. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarca Fil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California at San Diego; Estados Unidos

One hundred and sixty one healthy unrelated hematopoietic stem cell transplant donors from Western Indian were enrolled in this study. The study was initiated to observe the genotypic diversity of 16 killer immunoglobulin like receptor (KIR) genes in this population. KIR genotyping was carried out using the PCR-SSP technique. 56 KIR genotypes were observed in our population, where 15 genotypes were reported for the first time in any population. The KIR genotype data for the population can be accessed from Allele Frequencies Net Database under the population name "India Western KIR" and identifier "3570".

Background: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides. Methods: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level. Results: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore). Conclusion: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification. Fil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Karosiene, Edita. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Edwards, Lindy. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Grifoni, Alba. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina Fil: Weiskopf, Daniela. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Sidney, John. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina Fil: Peters, Bjoern. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados Unidos

The task of epitope discovery and vaccine design is increasingly reliant on bioinformatics analytic tools and access to depositories of curated data relevant to immune reactions and specific pathogens. The Immune Epitope Database and Analysis Resource (IEDB) was indeed created to assist biomedical researchers in the development of new vaccines, diagnostics, and therapeutics. The Analysis Resource is freely available to all researchers and provides access to a variety of epitope analysis and prediction tools. The tools include validated and benchmarked methods to predict MHC class I and class II binding. The predictions from these tools can be combined with tools predicting antigen processing, TCR recognition, and B cell epitope prediction. In addition, the resource contains a variety of secondary analysis tools that allow the researcher to calculate epitope conservation, population coverage, and other relevant analytic variables. The researcher involved in vaccine design and epitope discovery will also be interested in accessing experimental published data, relevant to the specific indication of interest. The database component of the IEDB contains a vast amount of experimentally derived epitope data that can be queried through a flexible user interface. The IEDB is linked to other pathogen-specific and immunological database resources.

In the past, numerous methods have been developed to predict MHC class II binders or T-helper epitopes for designing the epitope-based vaccines against pathogens. In contrast, limited attempts have been made to develop methods for predicting T-helper epitopes/peptides that can induce a specific type of cytokine. This paper describes a method, developed for predicting interleukin-10 (IL-10) inducing peptides, a cytokine responsible for suppressing the immune system. All models were trained and tested on experimentally validated 394 IL-10 inducing and 848 non-inducing peptides. It was observed that certain types of residues and motifs are more frequent in IL-10 inducing peptides than in non-inducing peptides. Based on this analysis, we developed composition-based models using various machine-learning techniques. Random Forest-based model achieved the maximum Matthews’s Correlation Coefficient (MCC) value of 0.59 with an accuracy of 81.24% developed using dipeptide composition. In order to facilitate the community, we developed a web server “IL-10pred”, standalone packages and a mobile app for designing IL-10 inducing peptides (http://crdd.osdd.net/raghava/IL-10pred/).

In this study, we describe a web-based resource, developed for assisting the scientific community in designing an effective therapeutics against the Ebola virus. Firstly, we predicted and identified experimentally validated epitopes in each of the antigens/proteins of the five known ebolaviruses. Secondly, we generated all the possible overlapping 9mer peptides from the proteins of ebolaviruses. Thirdly, conserved peptides across all the five ebolaviruses (four human pathogenic species) with no identical sequence in the human proteome, based on 1000 Genomes project, were identified. Finally, we identified peptide or epitope-based vaccine candidates that could activate both the B- and T-cell arms of the immune system. In addition, we also identified efficacious siRNAs against the mRNA transcriptome (absent in human transcriptome) of all the five ebolaviruses. It was observed that three species can potentially be targeted by a single siRNA (19mer) and 75 siRNAs can potentially target at least two species. A web server, EbolaVCR, has been developed that incorporates all the above information and useful computational tools (http://crdd.osdd.net/oscadd/ebola/).

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.