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A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

Supplementary Figures S1 to S5

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 2 residual tumor had a 5-year progression-free survival (PFS) of 72.7 ± 12.7% and OS of 81.8 ± 11%. Infants with M0 disease had a 5-year PFS of 39.1 ± 11.5% and OS of 51.8 ± 12%. Those with metastases fared poorly [5-year OS 25 ± 12.5% (children) and 0% (infants)]. SMARCB1 GLAs were not associated with PFS.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.

Germline methodology

The incidence of whooping cough (pertussis), the respiratory disease caused byBordetella pertussis(BP) has increased in recent years, and it is suspected that the switch from whole-cell pertussis (wP) to acellular pertussis (aP) vaccines may be a contributing factor to the rise in morbidity. While a growing body of evidence indicates that T cells play a role in the control and prevention of symptomatic disease, nearly all data on human BP-specific T cells is related to the four antigens contained in the aP vaccines, and data detailing T cell responses to additional non-aP antigens, are lacking. Here, we derived a full-genome map of human BP-specific CD4+ T cell responses using a high-throughputex vivoActivation Induced Marker (AIM) assay, to screen a peptide library spanning over 3000 different BP ORFs. First, our data show that BP specific-CD4+ T cells are associated with a large and previously unrecognized breadth of responses, including hundreds of targets. Notably, fifteen distinct non-aP vaccine antigens were associated with reactivity comparable to that of the aP vaccine antigens. Second, the overall pattern and magnitude of CD4+ T cell reactivity to aP and non-aP vaccine antigens was similar regardless of aP vs wP childhood vaccination history, suggesting that the profile of T cell reactivity in adults is not driven by vaccination, but rather is likely driven by subsequent asymptomatic or sub-clinical infections. Finally, while aP vaccine responses were Th1/Th2 polarized as a function of childhood vaccination, CD4+ T cell responses to non-aP BP antigens vaccine responses were not, suggesting that these antigens could be used to avoid the Th2 bias associated with aP vaccination. Overall, these findings enhance our understanding of human T cell responses against BP and suggest potential targets for designing next-generation pertussis vaccines.

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.

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

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.

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.

BackgroundMalaria is a life-threatening disease caused by protozoan parasite of genusPlasmodium. Various antigenic proteins ofPlasmodiumare considered as the major targets for the development of an effective vaccine. The aim of the current study was a comprehensive analysis of the experimentally validated epitopes ofPlasmodiumobtained from various immunoassays.MethodsPlasmodiumspecies epitopes were prefetched from Immune Epitope Database (IEDB). Species specific classification of available epitopes was done for both human and murine malaria parasites. Further, these T cell and B cell epitopes along with MHC I/II binders of differentPlasmodiumspecies were examined to find out their capability to induce IFN-γ and IL-10 using IFNepitope and IL-10 Pred, respectively.ResultsThe species-specific classification of 6874 unique epitopes resulted in the selection of predominant human and murinePlasmodiumspecies. Further, the attempt was made to analyse the immune reactivity of these epitopes for their ability to induce cytokines namely IFN-γ and IL-10. Total, 2775 epitopes were predicted to possess IFN-γ inducing ability, whereas 1275 epitopes were found to be involved in the induction of IL-10.ConclusionsThis study facilitates the assessment ofPlasmodiumepitopes and associated proteins as a potential approach to design and develop an epitope-based vaccine. Moreover, the results highlight the epitope-based immunization in malaria to induce a protective immune response.

Little is known about risks associated with germlineTo study tumour risks, we have analysed data of a large cohort of 45 unpublished patients with a germlineOverall, 117/172 (68%)Germline

BackgroundLittle is known about risks associated with germlineSUFUpathogenic variants (PVs) known as a cancer predisposition syndrome.MethodsTo study tumour risks, we have analysed data of a large cohort of 45 unpublished patients with a germlineSUFUPV completed with 127 previously published patients. To reduce the ascertainment bias due to index patient selection, the risk of tumours was evaluated in relatives withSUFUPV (89 patients) using the Nelson-Aalen estimator.ResultsOverall, 117/172 (68%)SUFUPV carriers developed at least one tumour: medulloblastoma (MB) (86 patients), basal cell carcinoma (BCC) (25 patients), meningioma (20 patients) and gonadal tumours (11 patients). Thirty-three of them (28%) had multiple tumours. Median age at diagnosis of MB, gonadal tumour, first BCC and first meningioma were 1.5, 14, 40 and 44 years, respectively. Follow-up data were available for 160 patients (137 remained alive and 23 died). The cumulative incidence of tumours in relatives was 14.4% (95% CI 6.8 to 21.4), 18.2% (95% CI 9.7 to 25.9) and 44.1% (95% CI 29.7 to 55.5) at the age of 5, 20 and 50 years, respectively. The cumulative risk of an MB, gonadal tumour, BCC and meningioma at age 50 years was: 13.3% (95% CI 6 to 20.1), 4.6% (95% CI 0 to 9.7), 28.5% (95% CI 13.4 to 40.9) and 5.2% (95% CI 0 to 12), respectively. Sixty-four different PVs were reported across the entireSUFUgene and inherited in 73% of cases in which inheritance could be evaluated.ConclusionGermlineSUFUPV carriers have a life-long increased risk of tumours with a spectrum dominated by MB before the age of 5, gonadal tumours during adolescence and BCC and meningioma in adulthood, justifying fine-tuned surveillance programmes.

Hematopoietic system (HS) is one of the most unique, adaptive and comprehensive developmental systems on which various other body systems relies on. It consists of a central pool of multipotent hematopoietic stem cells (HSCs) differentiating into lymphoid and myeloid lineage by series of gradual loss of stemness potential. Thus, this highly coordinated phenomenon of blood cell renewal ensures robust immunity and limits autoimmunity. Any disease, chronic infection or stress interrupts HS homeostasis and breaks HSCs’ dormancy, thereby activating HSCs to meet the peripheral demand for different immune cells via their expansion and differentiation into more lineage-restricted progenitors, primarily within the bone marrow (BM) in adult life. Therefore, a greater understanding of the overall regulatory landscape of HSC homeostasis and their perturbations is critical for dissecting protective immunity versus autoimmunity. Recent advancements in next-generation sequencing (NGS) viz genomic, transcriptomic, epigenomic and proteogenomic methods at bulk as well as single-cell levels have increased our apprehension for HSC working model. In this review, we discussed the recent findings and computational methods used to unravel the new HSC model revised over the classical model.

Lorlatinib Response in ALK-Fusion–Positive Glioma A child near death with an ALK-fusion–positive high-grade glioma refractory to standard treatment had a dramatic response when treatment with lorla...

Despite the prevalence and medical significance of human cytomegalovirus (HCMV) infections, a systematic analysis of the targets of T cell recognition in humans that spans the entire genome and includes recently described potential novel open reading frames (ORFs) is not available. Here, we screened a library of epitopes predicted to bind HLA class II that spans over 350 different HCMV ORFs and includes ∼150 previously described and ∼200 recently described potential novel ORFs by using an ex vivo gamma interferon (IFN-γ) FluoroSpot assay. We identified 235 unique HCMV-specific epitopes derived from 100 ORFs, some previously described as immunodominant and others that were not previously described to be immunogenic. Of those, 41 belong to the set of recently reported novel ORFs, thus providing evidence that at least some of these are actually expressed in vivo in humans. These data reveal that the breadth of the human T cell response to HCMV is much greater than previously thought. The ORFs and epitopes identified will help elucidate how T cell immunity relates to HCMV pathogenesis and instruct ongoing HCMV vaccine research. IMPORTANCE To understand the crucial role of adaptive immunity in controlling cytomegalovirus infection and disease, we systematically analyzed the CMV "ORFeome" to identify new CMV epitopes targeted primarily by CD4 T cells in humans. Our study identified >200 new T cell epitopes derived from both canonical and novel ORFs, highlighting the substantial breadth of the anti-CMV T cell response and providing new targets for vaccine design.

Bioinformatics has evolved from providing basic solutions, such as sequence alignment, structure predictions, and phylogenetic analysis to an independent data-driven field. The unprecedented growth of genomic technologies and the enormous data have opened an avenue for bioinformaticians (Bioinformatics professionals) never been seen before in the history of mankind. The novel opportunity also requires creative solutions that need skills to deal with noisy, unstructured information to offer valuable biological insights. Currently, we are seeing only the tip of an iceberg and the future will revolve around big data sets in all forms of biological research. The emerging challenge is to unfold the hidden iceberg of data.

Despite the prevalence and medical significance of human cytomegalovirus (HCMV) infections, a systematic analysis of the targets of T cell recognition in humans that spans the entire genome and includes recently described potential novel ORFs is not available. Here, we screened a library of epitopes predicted to bind HLA class II that spans over 350 different HCMV ORFs and includes ∼150 previously described and ∼200 recently described potential novel ORFs using an ex vivo IFNγ fluorospot assay. We identified 235 unique HCMV specific epitopes derived from 100 ORFs, some previously described as immunodominant and others that were not previously described to be immunogenic. Of those, 41 belong to the set of recently reported novel ORFs, thus providing evidence that at least some of these are actually expressed in vivo in humans. These data reveal that the breadth of the human T cell response to HCMV is much greater than previously thought. The ORFs and epitopes identified will help elucidate how T cell immunity relates to HCMV pathogenesis and instruct ongoing HCMV vaccine research.ImportanceTo understand the crucial role of adaptive immunity in controlling cytomegalovirus infection and disease, we systematically analyzed the CMV ’ORFeome’ to identify new CMV epitopes targeted primarily by CD4 T cells in humans. Our study identified >200 new T cell epitopes derived from both canonical and novel ORFs, highlighting the substantial breadth of anti-CMV T cell response and providing new targets for vaccine design.

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.

Dry eye disease (DED) can be represented as a display of disease in the mucosal part of the eye. It is quite distinct from the retinal side of the eye which connects with the neurons and thus represents the neuroimmunological disease. DED can occur either by the internal damage of the T cells inside the body or by microbial infections. Here we summarize the most common animal model systems used for DED relating to immune factors. We aimed to identify the most important immune cell/cytokine among the animal models of the disease. We also show the essential immune factors which are being tested for DED treatment. In our results, both the mechanism and the treatment of its animal models indicate the involvement of Th1 cells and the pro-inflammatory cytokine (IL-1β and TNF-α) related to the Th1-cells. The study is intended to increase the knowledge of the animal models in the field of the ocular surface along with the opening of a dimension of thoughts while designing a new animal model or treatment paradigm for ocular surface inflammatory disorders.

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.

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists". ispartof: F1000Res vol:8 pages:ISCB Comm J-1574- ispartof: location:England status: Published online

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

Motivation Datasets that are derived from different studies (e.g. MHC ligand elution, MHC binding, B/T cell epitope screening etc.) often vary in terms of experimental approaches, sizes of peptides tested, including partial and or nested overlapping peptides and in the number of donors tested. Results We present a customized application of the Immune Epitope Database’s ImmunomeBrowser tool, which can be used to effectively aggregate and visualize heterogeneous immunological data. User provided peptide sets and associated response data is mapped to a user-provided protein reference sequence. The output consists of tables and figures representing the aggregated data represented by a Response Frequency score and associated estimated confidence interval. This allows the user to visualizing regions associated with dominant responses and their boundaries. The results are presented both as a user interactive javascript based web interface and a tabular format in a selected reference sequence. Availability and implementation The ‘ImmunomeBrowser’ has been a longstanding feature of the IEDB (http://www.iedb.org). The present application extends the use of this tool to work with user-provided datasets, rather than the output of IEDB queries. This new server version of the ImmunomeBrowser is freely accessible at http://tools.iedb.org/immunomebrowser/.

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".

Tissue level expression, mutation, and substrate specificity of the transporter proteins have been widely accepted for their usefulness in drug disposition and efficacy. Many transporters play a significant role in normal human physiology as well as in disease conditions. Association of these properties, with systemic plasma concentration of the drug, is the leading reason for adverse drug reactions and drug resistance. The identification and validation of transporter proteins in experiments and their atomic resolution for characterization of structural-functional relationship is a costly, time-consuming, and more tedious process. However, predictive in silico tools claimed well for accurately accessing the pharmacokinetics, pharmacodynamics properties in early drug discovery stage. But the huge amount of data requires the development of reliable computational techniques and databases for the identification and/or prediction of membrane transport proteins as well as their ligands has become essential. Here, we review the available datasets and the computational methods, which put forth more insights for better understanding of human drug transporter proteins.

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

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

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/).

Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollar and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.

Due to advancement in sequencing technology, genomes of thousands of cancer tissues or cell-lines have been sequenced. Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. This paper describes a platform Cancertope, developed for designing genome-based immunotherapy or vaccine against a cancer cell. Broadly, the integrated resources on this platform are apportioned into three precise sections. First section explains a cancer-specific database of neoepitopes generated from genome of 905 cancer cell lines. This database harbors wide range of epitopes (e.g., B-cell, CD8+ T-cell, HLA class I, HLA class II) against 60 cancer-specific vaccine antigens. Second section describes a partially personalized module developed for predicting potential neoepitopes against a user-specific cancer genome. Finally, we describe a fully personalized module developed for identification of neoepitopes from genomes of cancerous and healthy cells of a cancer-patient. In order to assist the scientific community, wide range of tools are incorporated in this platform that includes screening of epitopes against human reference proteome (http://www.imtech.res.in/raghava/cancertope/).

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/).

Current Zika virus (ZIKV) outbreaks that spread in several areas of Africa, Southeast Asia, and in pacific islands is declared as a global health emergency by World Health Organization (WHO). It causes Zika fever and illness ranging from severe autoimmune to neurological complications in humans. To facilitate research on this virus, we have developed an integrative multi-omics platform; ZikaVR (http://bioinfo.imtech.res.in/manojk/zikavr/), dedicated to the ZIKV genomic, proteomic and therapeutic knowledge. It comprises of whole genome sequences, their respective functional information regarding proteins, genes, and structural content. Additionally, it also delivers sophisticated analysis such as whole-genome alignments, conservation and variation, CpG islands, codon context, usage bias and phylogenetic inferences at whole genome and proteome level with user-friendly visual environment. Further, glycosylation sites and molecular diagnostic primers were also analyzed. Most importantly, we also proposed potential therapeutically imperative constituents namely vaccine epitopes, siRNAs, miRNAs, sgRNAs and repurposing drug candidates.

In this study, we investigated drug profile of 24 anticancer drugs tested against a large number of cell lines in order to understand the relation between drug resistance and altered genomic features of a cancer cell line. We detected frequent mutations, high expression and high copy number variations of certain genes in both drug resistant cell lines and sensitive cell lines. It was observed that a few drugs, like Panobinostat, are effective against almost all types of cell lines, whereas certain drugs are effective against only a limited type of cell lines. Tissue-specific preference of drugs was also seen where a drug is more effective against cell lines belonging to a specific tissue. Genomic features based models have been developed for each anticancer drug and achieved average correlation between predicted and actual growth inhibition of cell lines in the range of 0.43 to 0.78. We hope, our study will throw light in the field of personalized medicine, particularly in designing patient-specific anticancer drugs. In order to serve the scientific community, a webserver, CancerDP, has been developed for predicting priority/potency of an anticancer drug against a cancer cell line using its genomic features (http://crdd.osdd.net/raghava/cancerdp/).

Our innate immune system recognizes a foreign RNA sequence of a pathogen and activates the immune system to eliminate the pathogen from our body. This immunomodulatory potential of RNA can be used to design RNA-based immunotherapy and vaccine adjuvants. In case of siRNA-based therapy, the immunomodulatory effect of an RNA sequence is unwanted as it may cause immunotoxicity. Thus, we developed a method for designing a single-stranded RNA (ssRNA) sequence with desired immunomodulatory potentials, for designing RNA-based therapeutics, immunotherapy and vaccine adjuvants. The dataset used for training and testing our models consists of 602 experimentally verified immunomodulatory oligoribonucleotides (IMORNs) that are ssRNA sequences of length 17 to 27 nucleotides and 520 circulating miRNAs as non-immunomodulatory sequences. We developed prediction models using various features that include composition-based features, binary profile, selected features and hybrid features. All models were evaluated using five-fold cross-validation and external validation techniques; achieving a maximum mean Matthews Correlation Coefficient (MCC) of 0.86 with 93% accuracy. We identified motifs using MERCI software and observed the abundance of adenine (A) in motifs. Based on the above study, we developed a web server, imRNA, comprising of various modules important for designing RNA-based therapeutics (http://crdd.osdd.net/raghava/imrna/).

UNLABELLED:Development of an effective vaccine against drug-resistant Mycobacterium tuberculosis (Mtb) is crucial for saving millions of premature deaths every year due to tuberculosis. This paper describes a web portal developed for assisting researchers in designing vaccines against emerging Mtb strains using traditional and modern approaches. Firstly, we annotated 59 genomes of Mycobacterium species to understand similarity/dissimilarity between tuberculoid, non-tuberculoid and vaccine strains at genome level. Secondly, antigen-based vaccine candidates have been predicted in each Mtb strain. Thirdly, epitopes-based vaccine candidates were predicted/discovered in above antigen-based vaccine candidates that can stimulate all arms of immune system. Finally, a database of predicted vaccine candidates at epitopes as well at antigen level has been developed for above strains. In order to design vaccine against a newly sequenced genome of Mtb strain, server integrates three modules for identification of strain-, antigen-, epitope-specific vaccine candidates. We observed that 103,522 unique peptides (9mers) had the potential to induce an antibody response and/or promiscuous binder to MHC alleles and/or have the capability to stimulate T lymphocytes. In summary, this web-portal will be useful for researchers working on designing vaccines against Mtb including drug-resistant strains. AVAILABILITY:The database is available freely at http://crdd.osdd.net/raghava/mtbveb/.

Immunomodulatory oligodeoxynucleotides (IMODNs) are the short DNA sequences that activate the innate immune system via toll-like receptor 9. These sequences predominantly contain unmethylated CpG motifs. In this work, we describe VaccineDA (Vaccine DNA adjuvants), a web-based resource developed to design IMODN-based vaccine adjuvants. We collected and analyzed 2193 experimentally validated IMODNs obtained from the literature. Certain types of nucleotides (e.g., T, GT, TC, TT, CGT, TCG, TTT) are dominant in IMODNs. Based on these observations, we developed support vector machine-based models to predict IMODNs using various compositions. The developed models achieved the maximum Matthews Correlation Coefficient (MCC) of 0.75 with an accuracy of 87.57% using the pentanucleotide composition. The integration of motif information further improved the performance of our model from the MCC of 0.75 to 0.77. Similarly, models were developed to predict palindromic IMODNs and attained a maximum MCC of 0.84 with the accuracy of 91.94%. These models were evaluated using a five-fold cross-validation technique as well as validated on an independent dataset. The models developed in this study were integrated into VaccineDA to provide a wide range of services that facilitate the design of DNA-based vaccine adjuvants (http://crdd.osdd.net/raghava/vaccineda/).

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.