1
|
|
2
|
Kaur D, Arora C, Raghava GPS. Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer. Mol Diagn Ther 2021; 25:629-646. [PMID: 34155607 DOI: 10.1007/s40291-021-00539-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2021] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs. OBJECTIVES The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature. METHODS This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes. RESULTS Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86. CONCLUSION We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.
Collapse
Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, New Delhi, 110020, India.
| |
Collapse
|
3
|
Kardani K, Bolhassani A, Namvar A. An overview of in silico vaccine design against different pathogens and cancer. Expert Rev Vaccines 2020; 19:699-726. [PMID: 32648830 DOI: 10.1080/14760584.2020.1794832] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy. AREAS COVERED In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer. EXPERT OPINION Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.
Collapse
Affiliation(s)
- Kimia Kardani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences , Tehran, Iran.,Department of Hepatitis and AIDS, Pasteur Institute of Iran , Tehran, Iran
| | - Azam Bolhassani
- Department of Hepatitis and AIDS, Pasteur Institute of Iran , Tehran, Iran
| | - Ali Namvar
- Iranian Comprehensive Hemophilia Care Center , Tehran, Iran
| |
Collapse
|
4
|
Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell 2020; 178:714-730.e22. [PMID: 31348891 DOI: 10.1016/j.cell.2019.06.029] [Citation(s) in RCA: 660] [Impact Index Per Article: 165.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 03/25/2019] [Accepted: 06/18/2019] [Indexed: 11/29/2022]
Abstract
Genome-wide association studies (GWAS) have revealed risk alleles for ulcerative colitis (UC). To understand their cell type specificities and pathways of action, we generate an atlas of 366,650 cells from the colon mucosa of 18 UC patients and 12 healthy individuals, revealing 51 epithelial, stromal, and immune cell subsets, including BEST4+ enterocytes, microfold-like cells, and IL13RA2+IL11+ inflammatory fibroblasts, which we associate with resistance to anti-TNF treatment. Inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and T cells that co-express CD8 and IL-17 expand with disease, forming intercellular interaction hubs. Many UC risk genes are cell type specific and co-regulated within relatively few gene modules, suggesting convergence onto limited sets of cell types and pathways. Using this observation, we nominate and infer functions for specific risk genes across GWAS loci. Our work provides a framework for interrogating complex human diseases and mapping risk variants to cell types and pathways.
Collapse
Affiliation(s)
| | - Moshe Biton
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Department of Molecular Biology, MGH, Boston, MA, USA
| | - Jose Ordovas-Montanes
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Institute for Medical Engineering and Science (IMES), MIT, Cambridge, MA, USA; Department of Chemistry, MIT, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA; Division of Infectious Diseases and Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA
| | - Keri M Sullivan
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Grace Burgin
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Daniel B Graham
- Department of Molecular Biology, MGH, Boston, MA, USA; Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, USA
| | - Rebecca H Herbst
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Noga Rogel
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Malika Sud
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Elizabeth Andrews
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Gabriella Velonias
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Adam L Haber
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | | | - Sanja Vickovic
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Junmei Yao
- Center for Computational and Integrative Biology, MGH, Boston, MA, USA
| | | | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Lan T Nguyen
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Alexandra-Chloé Villani
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Center for Immunology and Inflammatory Diseases, Department of Medicine, MGH, Boston, MA, USA
| | - Matan Hofree
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | | | - Hailiang Huang
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; Analytical and Translational Genetics Unit, MGH, Boston, MA, USA
| | | | - John J Garber
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Hamed Khalili
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - A Nicole Desch
- Broad Institute, Cambridge, MA, USA; Center for Computational and Integrative Biology, MGH, Boston, MA, USA
| | - Mark J Daly
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; Analytical and Translational Genetics Unit, MGH, Boston, MA, USA; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ashwin N Ananthakrishnan
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA.
| | - Alex K Shalek
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Institute for Medical Engineering and Science (IMES), MIT, Cambridge, MA, USA; Department of Chemistry, MIT, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA.
| | - Ramnik J Xavier
- Department of Molecular Biology, MGH, Boston, MA, USA; Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, USA; Center for Computational and Integrative Biology, MGH, Boston, MA, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA; Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA.
| |
Collapse
|
5
|
Kaur D, Arora C, Raghava GPS. A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information. Front Immunol 2020; 11:71. [PMID: 32082326 PMCID: PMC7002473 DOI: 10.3389/fimmu.2020.00071] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 01/13/2020] [Indexed: 12/17/2022] Open
Abstract
This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).
Collapse
Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| |
Collapse
|
6
|
Kaur D, Patiyal S, Sharma N, Usmani SS, Raghava GPS. PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5523871. [PMID: 31250014 PMCID: PMC6597477 DOI: 10.1093/database/baz076] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/19/2022]
Abstract
PRRDB 2.0 is an updated version of PRRDB that maintains comprehensive information about pattern-recognition receptors (PRRs) and their ligands. The current version of the database has ~2700 entries, which are nearly five times of the previous version. It contains extensive information about 467 unique PRRs and 827 pathogens-associated molecular patterns (PAMPs), manually extracted from ~600 research articles. It possesses information about PRRs and PAMPs that has been extracted manually from research articles and public databases. Each entry provides comprehensive details about PRRs and PAMPs that includes their name, sequence, origin, source, type, etc. We have provided internal and external links to various databases/resources (like Swiss-Prot, PubChem) to obtain further information about PRRs and their ligands. This database also provides links to ~4500 experimentally determined structures in the protein data bank of various PRRs and their complexes. In addition, 110 PRRs with unknown structures have also been predicted, which are important in order to understand the structure-function relationship between receptors and their ligands. Numerous web-based tools have been integrated into PRRDB 2.0 to facilitate users to perform different tasks like (i) extensive searching of the database; (ii) browsing or categorization of data based on receptors, ligands, source, etc. and (iii) similarity search using BLAST and Smith-Waterman algorithm.
Collapse
Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Salman Sadullah Usmani
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, Chandigarh 160036, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| |
Collapse
|
7
|
Mueller FB, Yang H, Lubetzky M, Verma A, Lee JR, Dadhania DM, Xiang JZ, Salvatore SP, Seshan SV, Sharma VK, Elemento O, Suthanthiran M, Muthukumar T. Landscape of innate immune system transcriptome and acute T cell-mediated rejection of human kidney allografts. JCI Insight 2019; 4:128014. [PMID: 31292297 PMCID: PMC6629252 DOI: 10.1172/jci.insight.128014] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 05/28/2019] [Indexed: 12/22/2022] Open
Abstract
Acute rejection of human allografts has been viewed mostly through the lens of adaptive immunity, and the intragraft landscape of innate immunity genes has not been characterized in an unbiased fashion. We performed RNA sequencing of 34 kidney allograft biopsy specimens from 34 adult recipients; 16 were categorized as Banff acute T cell-mediated rejection (TCMR) and 18 as normal. Computational analysis of intragraft mRNA transcriptome identified significantly higher abundance of mRNA for pattern recognition receptors in TCMR compared with normal biopsies, as well as increased expression of mRNAs for cytokines, chemokines, interferons, and caspases. Intragraft levels of calcineurin mRNA were higher in TCMR biopsies, suggesting underimmunosuppression compared with normal biopsies. Cell-type-enrichment analysis revealed higher abundance of dendritic cells and macrophages in TCMR biopsies. Damage-associated molecular patterns, the endogenous ligands for pattern recognition receptors, as well markers of DNA damage were higher in TCMR. mRNA expression patterns supported increased calcium flux and indices of endoplasmic, cellular oxidative, and mitochondrial stress were higher in TCMR. Expression of mRNAs in major metabolic pathways was decreased in TCMR. Our global and unbiased transcriptome profiling identified heightened expression of innate immune system genes during an episode of TCMR in human kidney allografts.
Collapse
Affiliation(s)
| | - Hua Yang
- Division of Nephrology and Hypertension, Department of Medicine
| | - Michelle Lubetzky
- Division of Nephrology and Hypertension, Department of Medicine
- Department of Transplantation Medicine
| | - Akanksha Verma
- Department of Physiology and Biophysics, Caryl and Israel Englander Institute for Precision Medicine, Institute for Computational Biomedicine
| | - John R. Lee
- Division of Nephrology and Hypertension, Department of Medicine
- Department of Transplantation Medicine
| | - Darshana M. Dadhania
- Division of Nephrology and Hypertension, Department of Medicine
- Department of Transplantation Medicine
| | - Jenny Z. Xiang
- Genomics Resources Core Facility, Department of Microbiology and Immunology; and
| | - Steven P. Salvatore
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College/NewYork–Presbyterian Hospital, New York, New York, USA
| | - Surya V. Seshan
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College/NewYork–Presbyterian Hospital, New York, New York, USA
| | - Vijay K. Sharma
- Division of Nephrology and Hypertension, Department of Medicine
| | - Olivier Elemento
- Department of Physiology and Biophysics, Caryl and Israel Englander Institute for Precision Medicine, Institute for Computational Biomedicine
| | - Manikkam Suthanthiran
- Division of Nephrology and Hypertension, Department of Medicine
- Department of Transplantation Medicine
| | - Thangamani Muthukumar
- Division of Nephrology and Hypertension, Department of Medicine
- Department of Transplantation Medicine
| |
Collapse
|
8
|
Vijay N, Chande A. A hypothetical new role for single-stranded DNA binding proteins in the immune system. Immunobiology 2018; 223:671-676. [DOI: 10.1016/j.imbio.2018.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 06/25/2018] [Accepted: 07/05/2018] [Indexed: 12/21/2022]
|
9
|
Usmani SS, Kumar R, Bhalla S, Kumar V, Raghava GPS. In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2018; 112:221-263. [PMID: 29680238 DOI: 10.1016/bs.apcsb.2018.01.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.
Collapse
Affiliation(s)
- Salman Sadullah Usmani
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Rajesh Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sherry Bhalla
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Vinod Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P S Raghava
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
| |
Collapse
|
10
|
Dhanda SK, Usmani SS, Agrawal P, Nagpal G, Gautam A, Raghava GPS. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform 2017; 18:467-478. [PMID: 27016393 DOI: 10.1093/bib/bbw025] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Indexed: 12/19/2022] Open
Abstract
The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.
Collapse
|
11
|
Chaudhary K, Nagpal G, Dhanda SK, Raghava GPS. Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Sci Rep 2016; 6:20678. [PMID: 26861761 PMCID: PMC4748260 DOI: 10.1038/srep20678] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 12/31/2015] [Indexed: 11/16/2022] Open
Abstract
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/).
Collapse
Affiliation(s)
| | - Gandharva Nagpal
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India
| | | | | |
Collapse
|
12
|
Nagpal G, Gupta S, Chaudhary K, Dhanda SK, Prakash S, Raghava GPS. VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Sci Rep 2015. [PMID: 26212482 PMCID: PMC4515643 DOI: 10.1038/srep12478] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
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/).
Collapse
Affiliation(s)
- Gandharva Nagpal
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| | - Sudheer Gupta
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| | - Kumardeep Chaudhary
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| | - Sandeep Kumar Dhanda
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| | - Satya Prakash
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| | - Gajendra P S Raghava
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, INDIA
| |
Collapse
|
13
|
Gupta S, Chavan S, Deobagkar DN, Deobagkar DD. Bio/chemoinformatics in India: an outlook. Brief Bioinform 2014; 16:710-31. [PMID: 25159593 DOI: 10.1093/bib/bbu028] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 07/28/2014] [Indexed: 12/25/2022] Open
Abstract
With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.
Collapse
|
14
|
Abstract
BACKGROUND The glycomics field has made great advancements in the last decade due to technologies for their synthesis and analysis including carbohydrate microarrays. Accordingly, databases for glycomics research have also emerged and been made publicly available by many major institutions worldwide. OBJECTIVE This review introduces these and other useful databases on which new methods for drug discovery can be developed. METHODS The scope of this review covers current documented and accessible databases and resources pertaining to glycomics. These were selected with the expectation that they may be useful for drug discovery research. RESULTS/CONCLUSION There is a plethora of glycomics databases that have much potential for drug discovery. This may seem daunting at first but this review helps to put some of these resources into perspective. Additionally, some thoughts on how to integrate these resources to allow more efficient research are presented.
Collapse
Affiliation(s)
- Kiyoko F Aoki-Kinoshita
- Associate Professor, Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-cho, Hachioji, Tokyo, 192-8577, Japan +81 42 691 4116 ; +81 42 691 4116 ;
| |
Collapse
|
15
|
Aithal A, Sharma A, Joshi S, Raghava GPS, Varshney GC. PolysacDB: a database of microbial polysaccharide antigens and their antibodies. PLoS One 2012; 7:e34613. [PMID: 22509333 PMCID: PMC3324500 DOI: 10.1371/journal.pone.0034613] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2011] [Accepted: 03/02/2012] [Indexed: 11/30/2022] Open
Abstract
Vaccines based on microbial cell surface polysaccharides have long been considered as attractive means to control infectious diseases. To realize this goal, detailed systematic information about the antigenic polysaccharide is necessary. However, only a few databases that provide limited knowledge in this area are available. This paper describes PolysacDB, a manually curated database of antigenic polysaccharides. We collected and compiled comprehensive information from literature and web resources about antigenic polysaccharides of microbial origin. The current version of the database has 1,554 entries of 149 different antigenic polysaccharides from 347 different microbes. Each entry provides comprehensive information about an antigenic polysaccharide, i.e., its origin, function, protocols for its conjugation to carriers, antibodies produced, details of assay systems, specificities of antibodies, proposed epitopes involved and antibody utilities. For convenience to the user, we have integrated web interface for searching, advanced searching and browsing data in database. This database will be useful for researchers working on polysaccharide-based vaccines. It is freely available from the URL: http://crdd.osdd.net/raghava/polysacdb/.
Collapse
Affiliation(s)
- Abhijit Aithal
- Cell Biology and Immunology Division, Institute of Microbial Technology, Chandigarh, India
| | - Arun Sharma
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India
| | - Shilpy Joshi
- Cell Biology and Immunology Division, Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P. S. Raghava
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India
- * E-mail: (GPSR); (GCV)
| | - Grish C. Varshney
- Cell Biology and Immunology Division, Institute of Microbial Technology, Chandigarh, India
- * E-mail: (GPSR); (GCV)
| |
Collapse
|
16
|
Gao QB, Zhao H, Ye X, He J. Prediction of pattern recognition receptor family using pseudo-amino acid composition. Biochem Biophys Res Commun 2011; 417:73-7. [PMID: 22138239 DOI: 10.1016/j.bbrc.2011.11.057] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Accepted: 11/12/2011] [Indexed: 01/21/2023]
Abstract
Pattern recognition receptors (PRRs) play a key role in the innate immune response by recognizing pathogen associated molecular patterns derived from a diverse collection of microbial pathogens. PRRs form a superfamily of proteins related to host health and disease. Thus, prediction of PRR family might supply biologically significant information for functional annotation of PRRs and development of novel drugs. In this paper, a computational method is proposed for predicting the families of PRRs. The prediction was performed on the basis of amino acid composition and pseudo-amino acid composition (PseAAC) from primary sequences of proteins using support vector machines. A non-redundant dataset consisted of 332 PRRs in seven families was constructed to do training and testing. It was demonstrated that different families of PRRs were quite closely correlated with amino acid composition as well as PseAAC. In the jackknife test, overall accuracies of amino acid composition-based and PseAAC-based classifiers reached 96.1% and 97.9%, respectively. The results indicate that families of PRRs are predictable with high accuracy. It is anticipated that this computational method might be a powerful tool for the automated assignment of families of PRRs.
Collapse
Affiliation(s)
- Qing-Bin Gao
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | | | | | | |
Collapse
|
17
|
Münk C, Sommer AF, König R. Systems-biology approaches to discover anti-viral effectors of the human innate immune response. Viruses 2011; 3:1112-30. [PMID: 21994773 PMCID: PMC3185791 DOI: 10.3390/v3071112] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Revised: 06/26/2011] [Accepted: 06/29/2011] [Indexed: 12/31/2022] Open
Abstract
Virus infections elicit an immediate innate response involving antiviral factors. The activities of some of these factors are, in turn, blocked by viral countermeasures. The ensuing battle between the host and the viruses is crucial for determining whether the virus establishes a foothold and/or induces adaptive immune responses. A comprehensive systems-level understanding of the repertoire of anti-viral effectors in the context of these immediate virus-host responses would provide significant advantages in devising novel strategies to interfere with the initial establishment of infections. Recent efforts to identify cellular factors in a comprehensive and unbiased manner, using genome-wide siRNA screens and other systems biology “omics” methodologies, have revealed several potential anti-viral effectors for viruses like Human immunodeficiency virus type 1 (HIV-1), Hepatitis C virus (HCV), West Nile virus (WNV), and influenza virus. This review describes the discovery of novel viral restriction factors and discusses how the integration of different methods in systems biology can be used to more comprehensively identify the intimate interactions of viruses and the cellular innate resistance.
Collapse
Affiliation(s)
- Carsten Münk
- Clinic for Gastroenterology, Hepatology and Infectiology, Medical Faculty, Heinrich Heine-University, Düsseldorf 40225, Germany; E-Mail:
| | - Andreas F.R. Sommer
- Research Group “Host-Pathogen Interactions”, Paul-Ehrlich-Institut, Langen 63225, Germany; E-Mail:
| | - Renate König
- Research Group “Host-Pathogen Interactions”, Paul-Ehrlich-Institut, Langen 63225, Germany; E-Mail:
- Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +49-6103-774019; Fax: +49-6103-771255
| |
Collapse
|
18
|
Linde A, Wachter B, Höner OP, Dib L, Ross C, Tamayo AR, Blecha F, Melgarejo T. Natural History of Innate Host Defense Peptides. Probiotics Antimicrob Proteins 2009; 1:97-112. [DOI: 10.1007/s12602-009-9031-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|
19
|
Ansari HR, Flower DR, Raghava GPS. AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Res 2009; 38:D847-53. [PMID: 19820110 PMCID: PMC2808902 DOI: 10.1093/nar/gkp830] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.
Collapse
Affiliation(s)
- Hifzur Rahman Ansari
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | | | | |
Collapse
|
20
|
Rashid M, Singla D, Sharma A, Kumar M, Raghava GPS. Hmrbase: a database of hormones and their receptors. BMC Genomics 2009; 10:307. [PMID: 19589147 PMCID: PMC2720991 DOI: 10.1186/1471-2164-10-307] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2008] [Accepted: 07/09/2009] [Indexed: 12/04/2022] Open
Abstract
Background Hormones are signaling molecules that play vital roles in various life processes, like growth and differentiation, physiology, and reproduction. These molecules are mostly secreted by endocrine glands, and transported to target organs through the bloodstream. Deficient, or excessive, levels of hormones are associated with several diseases such as cancer, osteoporosis, diabetes etc. Thus, it is important to collect and compile information about hormones and their receptors. Description This manuscript describes a database called Hmrbase which has been developed for managing information about hormones and their receptors. It is a highly curated database for which information has been collected from the literature and the public databases. The current version of Hmrbase contains comprehensive information about ~2000 hormones, e.g., about their function, source organism, receptors, mature sequences, structures etc. Hmrbase also contains information about ~3000 hormone receptors, in terms of amino acid sequences, subcellular localizations, ligands, and post-translational modifications etc. One of the major features of this database is that it provides data about ~4100 hormone-receptor pairs. A number of online tools have been integrated into the database, to provide the facilities like keyword search, structure-based search, mapping of a given peptide(s) on the hormone/receptor sequence, sequence similarity search. This database also provides a number of external links to other resources/databases in order to help in the retrieving of further related information. Conclusion Owing to the high impact of endocrine research in the biomedical sciences, the Hmrbase could become a leading data portal for researchers. The salient features of Hmrbase are hormone-receptor pair-related information, mapping of peptide stretches on the protein sequences of hormones and receptors, Pfam domain annotations, categorical browsing options, online data submission, DrugPedia linkage etc. Hmrbase is available online for public from .
Collapse
Affiliation(s)
- Mamoon Rashid
- Bioinformatics Centre, Institute of Microbial Technology, Chandigarh-160036, India.
| | | | | | | | | |
Collapse
|
21
|
Pitaluga AN, Beteille V, Lobo AR, Ortigão-Farias JR, Dávila AMR, Souza AA, Ramalho-Ortigão JM, Traub-Cseko YM. EST sequencing of blood-fed and Leishmania-infected midgut of Lutzomyia longipalpis, the principal visceral leishmaniasis vector in the Americas. Mol Genet Genomics 2009; 282:307-17. [PMID: 19565270 DOI: 10.1007/s00438-009-0466-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2008] [Accepted: 06/09/2009] [Indexed: 11/30/2022]
Abstract
Leishmaniasis is an important worldwide public health problem. Visceral leishmaniasis caused by Leishmania infantum chagasi is mainly transmitted by Lutzomyia longipalpis in the Americas. Leishmania development within the sand fly vector is mostly restricted to the midgut. Thus, a comparative analysis of blood-fed versus infected midguts may provide an invaluable insight into various aspects of sand fly immunity, physiology of blood digestion, and, more importantly, of Leishmania development. To that end, we have engaged in a study to identify expressed sequenced tags (ESTs) from L. longipalpis cDNA libraries produced from midguts dissected at different times post blood meal and also after artificial infection with L. i. chagasi. A total of 2,520 ESTs were obtained and, according to the quality of the sequencing data obtained, assembled into 378 clusters and 1,526 individual sequences or singletons totalizing 1,904 sequences. Several sequences associated with defense, apoptosis, RNAi, and digestion processes were annotated. The data presented here increases current knowledge on the New World sand fly transcriptome, contributing to the understanding of various aspects of the molecular physiology of L. longipalpis, and mechanisms underlying the relationship of this sand fly species with L. i. chagasi.
Collapse
Affiliation(s)
- André N Pitaluga
- Laboratório de Biologia Molecular de Tripanosomatídeos e Flebotomíneos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, RJ, Brazil
| | | | | | | | | | | | | | | |
Collapse
|
22
|
Abstract
Bacterial meningitis is still an important infectious disease with a high morbidity and mortality rate. Bacterial infection of the cerebrospinal fluid (CSF) space causes a powerful inflammatory reaction that is largely responsibly for meningitis-induced tissue damage and adverse outcome of the disease. In a landmark series of experiments in the mid-1980s, cell wall components including lipooligosaccharides and lipoteichoic acid were indicated to be the key bacterial elements that can trigger the host inflammatory response in the CSF. Ten years ago, the discovery of Toll-like receptor proteins (TLRs) that allow the detection of microbial components and initiate the host immune response opened up new horizons in research on the pathophysiology of meningitis. Cell culture approaches provided the first evidence for a crucial role of TLRs in sensing meningeal pathogens including Streptococcus pneumoniae, Neisseria meningitidis, Streptococcus agalactiae, and Listeria monocytogenes. Subsequently, studies in mice with single or combined deficiencies in TLRs demonstrated that TLR activation is a key event in meningeal inflammation and, even more interestingly, a pivotal factor for meningitis-associated tissue damage. A detailed understanding of the mechanisms of host-pathogen interactions in the CSF space may generate new opportunities for specific treatment strategies for bacterial meningitis.
Collapse
|
23
|
Jahnen-Dechent W, Simon U. Function follows form: shape complementarity and nanoparticle toxicity. Nanomedicine (Lond) 2008; 3:601-3. [DOI: 10.2217/17435889.3.5.601] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Willi Jahnen-Dechent
- Biomedical Engineering, Biointerface Laboratory, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Ulrich Simon
- Institute of Inorganic Chemistry, RWTH Aachen University, Landoltweg 1, 52056 Aachen, Germany
| |
Collapse
|