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

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

ViralVacDB: A manually curated repository of viral vaccines.

Over the years, numerous vaccines have been developed against viral infections; however, a complete database that provides comprehensive information on viral vaccines has been lacking. In this review, along with our freely accessible database ViralVacDB, we provide details of the viral vaccines, their type, routes of administration and approving agencies. This repository systematically covers additional information such as disease name, adjuvant, manufacturer, clinical status, age and dosage against 422 viral vaccines, including 145 approved vaccines and 277 in clinical trials. We anticipate that this database will be highly beneficial to researchers and others working in pharmaceuticals and immuno-informatics.

Identification of potential key genes related to idiopathic male infertility using RNA-sequencing data: an in-silico approach.

Among reproductive health problems, idiopathic infertility affects married couples. The current diagnosis of male infertility focuses on the concentration, motility, and morphology of sperm in the ejaculate. Since the molecular mechanism of idiopathic infertility is unknown, identification of Differentially Expressed Genes (DEGs) among the control and idiopathic infertile male can shed light on diagnosis and treatment. Here, we analyzed the dataset GSE65683 to identify DEGs in idiopathic human sperm in three groups of patients: (i) Timed Intercourse (TIC); (ii) Intrauterine Insemination (IUI); and (iii) Assisted Reproductive Technology (ART). The enrichment analysis was carried out using DAVID (Database for Annotation, Visualization and Integrated Discovery) and GeneCodis for the DEGs. Protein-Protein Interaction (PPI) network of these DEGs were constructed using the STRING database. The network parameters such as degree and betweenness were calculated to select the important hubs. In total, 118 DEGs in TIC, 446 in IUI, and 188 in ART were identified. PPI network was constructed and identified critical top hub genes such as ACTB, BTBD6, EIF2S3, EIF3A, EIF4E, POLR2L, RPL4, RPL7, RPS11, RPL13, RPS15, RPL23, RPL27, RPL9, RPLP0 and UBA52 that may play an essential role in idiopathic male infertility. Thus, the identified hub genes may provide an insight into the molecular mechanism and contribute to discovering novel therapeutic targets and developing new strategies for idiopathic male infertility.

Collection, compilation and analysis of bacterial vaccines.

BACKGROUND: Bacterial diseases are one of the leading causes of millions of fatalities worldwide, mainly due to antimicrobial resistance. The discovery of chicken cholera vaccine in 1879 revolutionized our fight against bacterial infections. Bacterial vaccines are proven to be highly effective in preventing many infectious diseases. Currently, various licensed vaccines are available against bacterial infections such as typhoid, diphtheria, cholera and tetanus in the market. In this study, we have attempted to compile different information regarding bacterial vaccines, their types, efficacy, mechanism of action, status, route of administration and other relevant details as a knowledgebase known as BacVacDB. METHODS: BacVacDB was implemented using Linux-Apache-MySQL-PHP. HTML, PHP, CSS and Javascript have been used to develop the front end and MySQL for the back end. The data was curated from several sources, including literature, databases and relevant web resources. RESULTS: This paper reviewed 371 vaccines against 30 human bacterial diseases maintained in BacVacDB, of which 167 are approved and 204 in clinical trials. This database provides the users an effortless search facility in the four modules, 'Search,' 'Browse,' 'External Links' and 'General Information'. In this systematic attempt, we also included the history of vaccines, their mechanism, types, route of administration and approving agencies. CONCLUSIONS: This knowledgebase has an intuitive interface that allows users to explore, search, and download information as well as to submit new bacterial vaccines (https://webs.iiitd.edu.in/raghava/bacvacdb/).

ToxinPred2: an improved method for predicting toxicity of proteins.

Proteins/peptides have shown to be promising therapeutic agents for a variety of diseases. However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study describes a web-based tool, ToxinPred2, developed for predicting the toxicity of proteins. This is an update of ToxinPred developed mainly for predicting toxicity of peptides and small proteins. The method has been trained, tested and evaluated on three datasets curated from the recent release of the SwissProt. To provide unbiased evaluation, we performed internal validation on 80% of the data and external validation on the remaining 20% of data. We have implemented the following techniques for predicting protein toxicity; (i) Basic Local Alignment Search Tool-based similarity, (ii) Motif-EmeRging and with Classes-Identification-based motif search and (iii) Prediction models. Similarity and motif-based techniques achieved a high probability of correct prediction with poor sensitivity/coverage, whereas models based on machine-learning techniques achieved balance sensitivity and specificity with reasonably high accuracy. Finally, we developed a hybrid method that combined all three approaches and achieved a maximum area under receiver operating characteristic curve around 0.99 with Matthews correlation coefficient 0.91 on the validation dataset. In addition, we developed models on alternate and realistic datasets. The best machine learning models have been implemented in the web server named 'ToxinPred2', which is available at https://webs.iiitd.edu.in/raghava/toxinpred2/ and a standalone version at https://github.com/raghavagps/toxinpred2. This is a general method developed for predicting the toxicity of proteins regardless of their source of origin.

In silico tools and databases for designing cancer immunotherapy.

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10-4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

Ebolabase: Zaire ebolavirus-human protein interaction database for drug-repurposing.

Ebola Virus (EBOV) is one of the deadliest pathogenic virus which causes hemorrhagic fever. Though many Ebola-human interaction studies and databases are already reported, the unavailability of an adequate model and lack of publically accessible resources requires a comprehensive study to curate the Ebola-Human-Drug interactions. In total, 270 human proteins interacted with EBOV are collected from published experimental evidence. Then the protein-protein interaction networks are generated as EBOV-human and EBOV-Human-Drugs interaction. These results can help the researcher to find the effective repurposed drug for EBOV treatment. Further, the illustration of gene enrichment and pathway analysis would provide knowledge and insight of EBOV-human interaction describes the importance of the study. Investigating the networks may help to identify a suitable human-based drug target for ebola research community. The inclusion of an emerging concept, a human-based drug targeted therapy plays a very significant role in drug repurposing which reduces the time and effort is the highlight of the current research. An integrated database namely, Ebolabase has been developed and linked with other repositories such as Epitopes, Structures, Literature, Genomics and Proteomics. All generated networks are made to be viewed in a customized manner and the required data can be downloaded freely. The Ebolabase is available at http://ebola.bicpu.edu.in.

Understanding of Zaire ebolavirus-human protein interaction for drug repurposing.

The Ebola virus is a human aggressive pathogen causes Ebola virus disease that threatens public health, for which there is no Food Drug Administration approved medication. Drug repurposing is an alternative method to find the novel indications of known drugs to treat the disease effectively at low cost. The present work focused on understanding the host-virus interaction as well as host virus drug interaction to identify the disease pathways and host-directed drug targets. Thus, existing direct physical Ebola-human protein-protein interaction (PPI) was collected from various publicly available databases and also literature through manual curation. Further, the functional and pathway enrichment analysis for the proteins were performed using database for annotation, visualization, and integrated discovery and the enriched gene ontology biological process terms includes chromatin assembly or disassembly, nucleosome organization, nucleosome assembly. Also, the enriched Kyoto Encyclopedia of Genes and Genome pathway terms includes systemic lupus erythematosus, alcoholism, and viral carcinogenesis. From the PPI network, important large histone clusters and tubulin were observed. Further, the host-virus and host-virus-drug interaction network has been generated and found that 182 drugs are associated with 45 host genes. The obtained drugs and their interacting targets could be considered for Ebola treatment.

Comprehensive analysis of dysregulated lncRNAs and their competing endogenous RNA network in triple-negative breast cancer.

The study aimed to explore the molecular mechanism underlying triple-negative breast cancer (TNBC) and to identify their potential diagnostic/prognostic biomarkers. The differentially expressed lncRNAs (DElncRNAs) were identified by meta-analysis and machine learning feature selection methods. The dysregulated lncRNA-miRNA-mRNA network was constructed based on the competing endogenous RNA (ceRNA) hypothesis. A total of 26 DElncRNAs were identified with a meta-analysis approach of which 18 DElncRNAs attained high accuracy in training and test dataset by Support Vector Machine-Recursive Feature Elimination (SVM-RFE) which could act as diagnostic biomarkers. Among the identified DElncRNAs, LINC01315 and CTA-384D8.35 could act as prognostic biomarkers. Finally, two important sub-modules from lncRNA-miRNA-mRNA network were identified which consists of DElncRNAs (LINC01087, LINC01315, and SOX9-AS1) interacting with co-expressed DEmRNAs and DEmiRNAs. Thus, the study indicated the importance of DElncRNAs and highlighted the efficacy as potential biomarkers in TNBC.

Identification of dysregulated miRNAs in triple negative breast cancer: A meta-analysis approach.

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying. Therefore, this study used meta-analysis to amalgamate ten miRNA profiling studies of TNBC. By the robust rank aggregation method, metasignatures of six miRNAs (4 upregulated: hsa-miR-135b-5p, hsa-miR-18a-5p, hsa-miR-9-5p and hsa-miR-522-3p; 2 downregulated: hsa-miR-190b and hsa-miR-449a) were obtained. The gene ontology analysis revealed that target genes regulated by miRNAs were associated with processes like the regulation of transcription, DNA dependent, and signal transduction. Also, it is noted from the pathway analysis that signaling and cancer pathways were associated with the progression of TNBC. A Naïve Bayes-based classifier built with miRNA signatures discriminates TNBC and non-TNBC samples in test data set with high diagnostic sensitivity and specificity. From the analysis carried out by the study, it is suggested that the identified miRNAs are of great importance in improving the diagnostics and therapeutics for TNBC.

Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non-TNBC and 405 TNBC samples were mined from Gene Expression Omnibus. The data sets were integrated by meta-analysis and identified 1075 differentially expressed genes. Protein-protein interaction network was constructed which consists of 486 nodes and 1932 edges, where 29 hub genes were obtained with high topological measures. Further, 16 features (hub genes), 12 upregulated (AURKB, CCNB2, CDC20, DDX18, EGFR, ENO1, MYC, NUP88, PLK1, PML, POLR2F, and SKP2) and four downregulated ( CCND1, GLI3, SKP1, and TGFB3) were selected through machine learning correlation based feature selection method on training data set. A naïve Bayes based classifier built using the expression profiles of 16 features (hub genes) accurately and reliably classify TNBC from non-TNBC samples in the validation test data set with a receiver operating curve of 0.93 to 0.98. Subsequently, Gene Ontology analysis revealed that the hub genes were enriched in mitotic cell cycle processes and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that they were enriched in cell cycle pathways. Thus, the identified key hub genes and pathways highlighted in the study would enhance the understanding of molecular mechanism of TNBC which may serve as potential therapeutic target.

Identification of Hub Genes and Pathways in Zika Virus Infection Using RNA-Seq Data: A Network-Based Computational Approach.

Zika virus (ZIKV), a single-strand RNA flavivirus, is transmitted primarily through Aedes aegypti. The recent outbreaks in America and unexpected association between ZIKV infection and birth defects have triggered the global attention. This vouches to understand the molecular mechanisms of ZIKV infection to develop effective drug therapy. A systems-level understanding of biological process affected by ZIKV infection in fetal brain sample led us to identify the candidate genes for pharmaceutical intervention and potential biomarkers for diagnosis. To identify the key genes, transcriptomics data (RNA-Seq) with GSE93385 of ZIKV (Strain: MR766) infected human fetal neural stem cell are analyzed. In total, 1,084 differentially expressed genes (DEGs) are identified, that is, 471 upregulated and 613 downregulated genes. Further analysis such as the gene ontology term suggested that the downregulated genes are mostly enriched in defense response to virus, receptor binding, laminin binding, extracellular matrix, endoplasmic reticulum, and for upregulated DEGs: translation initiation, RNA binding, cytosol, and nucleosome are enriched. And through pathway analysis, systemic lupus erythematosus (SLE) is found to be the most enriched pathway. Protein-protein interaction (PPI) network is constructed to find the hub genes using STRING database. The seven key genes namely cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), histone cluster 1 H2B family member K, (HIST1H2BK) histone cluster 1 H2B family member O (HIST1H2BO), and histone cluster 1 H2B family member B (HIST1H2BB), polo-like kinase 1 (PLK1), and cell division cycle 20 (CDC20) with highest degree are found to be hub genes using Centiscape, a Cytoscape plugin. The modules of PPI network using Molecular Complex Detection plugin are found significant in structural constituent of ribosome, defense response to virus, nucleosome, SLE, extracellular region, and regulation of gene silencing. Thus, identified key hub genes and pathways shed light on molecular mechanism that may contribute to the discovery of novel therapeutic targets and development of new strategies for the intervention of ZIKV disease.

ZikaBase: An integrated ZIKV- Human Interactome Map database.

Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today. This study made an attempt to curate the ZIKV-Human interaction proteins from published literatures and RNA-Seq data. 11 direct interaction, 12 associated genes are retrieved from literatures and 3742 Differentially Expressed Genes (DEGs) are obtained from RNA-Seq analysis. The genes have been analyzed to construct the ZIKV-Human Interactome Map. The importance of the study has been illustrated by the enrichment analysis and observed that direct interaction and associated genes are enriched in viral entry into host cell. Also, ZIKV infection modulates 32% signal and 27% immune system pathways. The integrated database, ZikaBase has been developed to help the virology research community and accessible at https://test5.bicpu.edu.in.

Expression Data Analysis for the Identification of Potential Biomarker of Pregnancy Associated Breast Cancer.

Breast cancer affects every 1 of 3000 pregnant women or in the first post-partum year is referred as Pregnancy Associated Breast Cancer (PABC) in mid 30s. Even-though rare disease, classified under hormone receptor negative status which metastasis quickly to other parts by extra cellular matrix degradation. Hence it is important to find an optimal treatment option for a PABC patient. Also additional care should be taken to choose the drug; in order to avoid fetal malformation and post-partum stage side-effects. The adaptation of target based therapy in the clinical practice may help to substitute the mastectomy treatment. Recent studies suggested that certain altered Post Translational Modifications (PTMs) may be an indicative of breast cancer progression; an attempt is made to consider the over represented PTM as a parameter for gene selection. The public dataset of PABC from GEO were examined to select Differentially Expressed Genes (DEG). The corresponding PTMs for DEG were collected and association between them was found using data mining technique. Usually clustering algorithm has been applied for the study of gene expression with drawback of clustering of gene products based on specified features. But association rule mining method overcome this shortcoming and determines the useful and in depth relationships. From the association, genes were selected to study the interactions and pathways. These studies emphasis that the genes KLF12, FEN1 MUC1 and SP110, can be chosen as target, which control cancer development, without any harm to pregnancy as well as fetal developmental process.