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Kaur D, Arora A, Patiyal S, Raghava GPS. Hmrbase2: a comprehensive database of hormones and their receptors. Hormones (Athens) 2023; 22:359-366. [PMID: 37291365 DOI: 10.1007/s42000-023-00455-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/19/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones. METHODS Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature. RESULTS Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. CONCLUSIONS To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .
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Affiliation(s)
- Dashleen Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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Singh A, Duche RT, Wandhare AG, Sian JK, Singh BP, Sihag MK, Singh KS, Sangwan V, Talan S, Panwar H. Milk-Derived Antimicrobial Peptides: Overview, Applications, and Future Perspectives. Probiotics Antimicrob Proteins 2023; 15:44-62. [PMID: 36357656 PMCID: PMC9649404 DOI: 10.1007/s12602-022-10004-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2022] [Indexed: 11/13/2022]
Abstract
The growing consumer awareness towards healthy and safe food has reformed food processing strategies. Nowadays, food processors are aiming at natural, effective, safe, and low-cost substitutes for enhancing the shelf life of food products. Milk, besides being a rich source of nutrition for infants and adults, serves as a readily available source of precious functional peptides. Due to the existence of high genetic variability in milk proteins, there is a great possibility to get bioactive peptides with varied properties. Among other bioactive agents, milk-originated antimicrobial peptides (AMPs) are gaining interest as attractive and safe additive conferring extended shelf life to minimally processed foods. These peptides display broad-spectrum antagonistic activity against bacteria, fungi, viruses, and protozoans. Microbial proteolytic activity, extracellular peptidases, food-grade enzymes, and recombinant DNA technology application are among few strategies to tailor specific peptides from milk and enhance their production. These bioprotective agents have a promising future in addressing the global concern of food safety along with the possibility to be incorporated into the food matrix without compromising overall consumer acceptance. Additionally, in conformity to the current consumer demands, these AMPs also possess functional properties needed for value addition. This review attempts to present the basic properties, synthesis approaches, action mechanism, current status, and prospects of antimicrobial peptide application in food, dairy, and pharma industry along with their role in ensuring the safety and health of consumers.
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Affiliation(s)
- Anamika Singh
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India
| | - Rachael Terumbur Duche
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India ,Department of Microbiology, Federal University of Agriculture, Makurdi, Nigeria
| | - Arundhati Ganesh Wandhare
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India
| | - Jaspreet Kaur Sian
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India ,Department of Microbiology, Punjab Agricultural University (PAU), Ludhiana, 141001 Punjab India
| | - Brij Pal Singh
- Department of Microbiology, Central University of Haryana, Mahendergarh, 123031 Haryana India
| | - Manvesh Kumar Sihag
- Department of Dairy Chemistry, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India
| | - Kumar Siddharth Singh
- Institute for Microbiology, Gottfried Wilhelm Leibniz University, Herrenhäuser Str. 2, 30419 Hanover, Germany
| | - Vikas Sangwan
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001 Punjab India
| | - Shreya Talan
- Dairy Microbiology Division, ICAR-National Dairy Research Institute (ICAR-NDRI), Karnal, Haryana India
| | - Harsh Panwar
- Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, 141001, Punjab, India.
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Jadhav A, Kumar T, Raghavendra M, Loganathan T, Narayanan M. Predicting cross-tissue hormone-gene relations using balanced word embeddings. Bioinformatics 2022; 38:4771-4781. [PMID: 36000859 PMCID: PMC9563690 DOI: 10.1093/bioinformatics/btac578] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing. RESULTS We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone's production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue-tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well. AVAILABILITY AND IMPLEMENTATION Freely available at https://cross-tissue-signaling.herokuapp.com are our model predictions & datasets; https://github.com/BIRDSgroup/BioEmbedS has all relevant code. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aditya Jadhav
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Tarun Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
| | - Mohit Raghavendra
- Department of Information Technology, National Institute of Technology Karnataka, Surathkal, India
| | - Tamizhini Loganathan
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- Initiative for Biological Systems Engineering, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
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Zhu N, Dong F, Shi G, Lao X, Zheng H. HORDB a comprehensive database of peptide hormones. Sci Data 2022; 9:187. [PMID: 35469024 PMCID: PMC9039076 DOI: 10.1038/s41597-022-01287-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
Peptide hormones (also known as hormone peptides and polypeptide hormones) are hormones composed of peptides and are signal transduction molecules produced by a class of multicellular organisms. It plays an important role in the physiological and behavioral regulation of animals and humans as well as in the growth of plants. In order to promote the research on peptide hormones, we constructed HORDB database. The database currently has a total of 6024 entries, including 5729 peptide hormones, 40 peptide drugs and 255 marketed pharmaceutical preparations information. Each entry provided comprehensive information related to the peptide, including general information, sequence, activity, structure, physical information and literature information. We also added information on IC50, EC50, ED50, target, and whether or not the blood-brain barrier was crossed to the activity information note. In addition, HORDB integrates search and sequence analysis to facilitate user browsing and data analysis. We believe that the peptide hormones information collected by HORDB will promote the design and discovery of peptide hormones, All data are hosted and available in figshare 10.6084/m9.figshare.c.5522241. Measurement(s) | peptide hormone | Technology Type(s) | Comprehensive data website service | Factor Type(s) | peptide hormone |
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Affiliation(s)
- Ning Zhu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Fanyi Dong
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Guobang Shi
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Xingzhen Lao
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
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Abstract
An important capacity of genes is the rapid change of expression levels to cope with the environment, known as expression responsiveness or plasticity. Elucidating the genomic mechanisms determining expression plasticity is critical for understanding the molecular basis of phenotypic plasticity, fitness and adaptation. In this study, we systematically quantified gene expression plasticity in four metazoan species by integrating changes of expression levels under a large number of genetic and environmental conditions. From this, we demonstrated that expression plasticity measures a distinct feature of gene expression that is orthogonal to other well-studied features, including gene expression level and tissue specificity/broadness. Expression plasticity is conserved across species with important physiological implications. The magnitude of expression plasticity is highly correlated with gene function and genes with high plasticity are implicated in disease susceptibility. Genome-wide analysis identified many conserved promoter cis-elements, trans-acting factors (such as CTCF), and gene body histone modifications (H3K36me3, H3K79me2 and H4K20me1) that are significantly associated with expression plasticity. Analysis of expression changes in perturbation experiments further validated a causal role of specific transcription factors and histone modifications. Collectively, this work reveals the general properties, physiological implications and multivariable regulation of gene expression plasticity in metazoans, extending the mechanistic understanding of gene regulation.
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Affiliation(s)
- Long Xiao
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 10049, People's Republic of China
| | - Zhiguang Zhao
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 10049, People's Republic of China
| | - Fei He
- Biology Department, Brookhaven National Lab, Upton, NY 11967, USA
| | - Zhuo Du
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, People's Republic of China.,University of Chinese Academy of Sciences, Beijing 10049, People's Republic of China
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Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev 2019; 99:1819-1875. [PMID: 31434538 DOI: 10.1152/physrev.00035.2018] [Citation(s) in RCA: 472] [Impact Index Per Article: 94.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.
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Affiliation(s)
- David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
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Anekthanakul K, Senachak J, Hongsthong A, Charoonratana T, Ruengjitchatchawalya M. Natural ACE inhibitory peptides discovery from Spirulina (Arthrospira platensis) strain C1. Peptides 2019; 118:170107. [PMID: 31229668 DOI: 10.1016/j.peptides.2019.170107] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/19/2019] [Accepted: 06/19/2019] [Indexed: 02/07/2023]
Abstract
Bioactive peptides from natural sources are utilized as food supplements for disease prevention and are increasingly becoming targets for drug discovery due to their specificity, efficacy and the absence of undesirable side effects, among others. Hence, the 'SpirPep' platform was developed to facilitate the in silico-based bioactive peptide discovery of these highly sought-after biomolecules from Spirulina(Arthrospira platensis) and to select the protease (thermolysin) used for in vitro digestion. Analysis of the predicted and experimentally-derived peptides suggested that they were mainly involved in ACE inhibition; thus, an ACEi assay was used to study the ACE inhibitory activity of five candidate peptides (SpirPep1-5), chosen from common peptides with multifunctional bioactivity and 100% bioactive peptide coverage, originating from phycobiliproteins. Results showed that SpirPep1 inhibited the activity of ACE with IC50 of 1.748 mM and was non-toxic to fibroblasts of African green monkey kidney and human dermal skin. The molecular docking and MD simulation analysis revealed SpirPep1 had significantly lower binding scores than others and showed greater specificity to ACE. The non-bonded interaction energy of SpirPep1 and ACE was -883 kJ/mol. The SpirPep1 indirectly bound to ACE via the ACE substrate binding sites residues (D121, E123, S516, and S517) found in natural ACE inhibitory peptides (angiotensin II and bradykinin potentiating peptides). In addition, two unreported substrate binding sites including R124 and S219 were found. These results indicate that 'SpirPep' platform could increase the success rate for natural bioactive peptide discovery.
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Affiliation(s)
- Krittima Anekthanakul
- Biotechnology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Thailand
| | - Jittisak Senachak
- Biosciences and Systems Biology Research Team, Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut's University of Technology Thonburi, Thailand
| | - Apiradee Hongsthong
- Biosciences and Systems Biology Research Team, Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut's University of Technology Thonburi, Thailand
| | | | - Marasri Ruengjitchatchawalya
- Biotechnology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Thailand; Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Thailand.
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8
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Panyayai T, Ngamphiw C, Tongsima S, Mhuantong W, Limsripraphan W, Choowongkomon K, Sawatdichaikul O. FeptideDB: A web application for new bioactive peptides from food protein. Heliyon 2019; 5:e02076. [PMID: 31372542 PMCID: PMC6656964 DOI: 10.1016/j.heliyon.2019.e02076] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 06/11/2019] [Accepted: 07/08/2019] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Bioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from 'enzyme digestion module' to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach. RESULTS The FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme). CONCLUSIONS FeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.
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Affiliation(s)
- Thitima Panyayai
- Genetic Engineering Interdisciplinary Program, Graduate School, Kasetsart University, 50 Ngam Wong Wan Rd, Bangkok, Chatuchak, 10900, Thailand
- Department of Research and Development, Betagro Science Center Co. Ltd., Klong Luang, Pathumthani, 12120, Thailand
| | - Chumpol Ngamphiw
- National Biobank of Thailand, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Sissades Tongsima
- National Biobank of Thailand, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Wuttichai Mhuantong
- Enzyme Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phahonyothin Road Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Wachira Limsripraphan
- Department of Computer Engineering, Faculty of Industrial Technology, Pibulsongkram Rajabhat University, 156 Mu 5 Plaichumpol Sub-district, Muang District, Phitsanulok, 65000, Thailand
| | - Kiattawee Choowongkomon
- Department of Biochemistry, Faculty of Science, Kasetsart University, 50 Ngam, Wong Wan Rd, Bangkok, Chatuchak, 10900, Thailand
- Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced Studies, Kasetsart University, Bangkok, 10900, Thailand
| | - Orathai Sawatdichaikul
- Department of Nutrition and Health, Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Ladyaow, Chatuchak, Bangkok, 10900, Thailand
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Réau M, Lagarde N, Zagury JF, Montes M. Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile. J Med Chem 2018; 62:2894-2904. [PMID: 30354114 DOI: 10.1021/acs.jmedchem.8b01105] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Nuclear receptors (NRs) are transcription factors that regulate gene expression in various physiological processes through their interactions with small hydrophobic molecules. They constitute an important class of targets for drugs and endocrine disruptors and are widely studied for both health and environment concerns. Since the integration of negative data can be critical for accurate modeling of ligand activity profiles, we manually collected and annotated NRs interaction data (positive and negative) through a sharp review of the corresponding literature. 15 116 positive and negative interactions data are provided for 28 NRs together with 593 PDB structures in the freely available Nuclear Receptors Database Including Negative Data ( http://nr-dbind.drugdesign.fr ). The NR-DBIND contains the most extensive information about interaction data on NRs, which should bring valuable information to chemists, biologists, pharmacologists and toxicologists.
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Affiliation(s)
- Manon Réau
- Laboratoire GBA, EA4627 , Conservatoire National des Arts et Métiers , 2 Rue Conté , 75003 Paris , France
| | - Nathalie Lagarde
- Laboratoire GBA, EA4627 , Conservatoire National des Arts et Métiers , 2 Rue Conté , 75003 Paris , France.,Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques in Silico, INSERM UMR-S 973, 75205 Paris , France
| | - Jean-François Zagury
- Laboratoire GBA, EA4627 , Conservatoire National des Arts et Métiers , 2 Rue Conté , 75003 Paris , France
| | - Matthieu Montes
- Laboratoire GBA, EA4627 , Conservatoire National des Arts et Métiers , 2 Rue Conté , 75003 Paris , France
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Agrawal P, Raghava GPS. Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure. Front Microbiol 2018; 9:2551. [PMID: 30416494 PMCID: PMC6212470 DOI: 10.3389/fmicb.2018.02551] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 10/05/2018] [Indexed: 12/14/2022] Open
Abstract
Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).
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Affiliation(s)
- Piyush Agrawal
- CSIR-Institute of Microbial Technology, Chandigarh, India.,Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
| | - Gajendra P S Raghava
- Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi, India
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11
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Kalmykova SD, Arapidi GP, Urban AS, Osetrova MS, Gordeeva VD, Ivanov VT, Govorun VM. In Silico Analysis of Peptide Potential Biological Functions. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2018. [DOI: 10.1134/s106816201804009x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Anekthanakul K, Hongsthong A, Senachak J, Ruengjitchatchawalya M. SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database. BMC Bioinformatics 2018; 19:149. [PMID: 29678128 PMCID: PMC5910554 DOI: 10.1186/s12859-018-2143-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Accepted: 04/03/2018] [Indexed: 12/22/2022] Open
Abstract
Background Bioactive peptides, including biological sources-derived peptides with different biological activities, are protein fragments that influence the functions or conditions of organisms, in particular humans and animals. Conventional methods of identifying bioactive peptides are time-consuming and costly. To quicken the processes, several bioinformatics tools are recently used to facilitate screening of the potential peptides prior their activity assessment in vitro and/or in vivo. In this study, we developed an efficient computational method, SpirPep, which offers many advantages over the currently available tools. Results The SpirPep web application tool is a one-stop analysis and visualization facility to assist bioactive peptide discovery. The tool is equipped with 15 customized enzymes and 1–3 miscleavage options, which allows in silico digestion of protein sequences encoded by protein-coding genes from single, multiple, or genome-wide scaling, and then directly classifies the peptides by bioactivity using an in-house database that contains bioactive peptides collected from 13 public databases. With this tool, the resulting peptides are categorized by each selected enzyme, and shown in a tabular format where the peptide sequences can be tracked back to their original proteins. The developed tool and webpages are coded in PHP and HTML with CSS/JavaScript. Moreover, the tool allows protein-peptide alignment visualization by Generic Genome Browser (GBrowse) to display the region and details of the proteins and peptides within each parameter, while considering digestion design for the desirable bioactivity. SpirPep is efficient; it takes less than 20 min to digest 3000 proteins (751,860 amino acids) with 15 enzymes and three miscleavages for each enzyme, and only a few seconds for single enzyme digestion. Obviously, the tool identified more bioactive peptides than that of the benchmarked tool; an example of validated pentapeptide (FLPIL) from LC-MS/MS was demonstrated. The web and database server are available at http://spirpepapp.sbi.kmutt.ac.th. Conclusion SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides. Electronic supplementary material The online version of this article (10.1186/s12859-018-2143-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Krittima Anekthanakul
- Biotechnology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand
| | - Apiradee Hongsthong
- Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology at King Mongkut's University of Technology Thonburi, 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand
| | - Jittisak Senachak
- Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology at King Mongkut's University of Technology Thonburi, 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand
| | - Marasri Ruengjitchatchawalya
- Biotechnology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand. .,Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd., Tha Kham, Bang Khun Thian, Bangkok, 10150, Thailand.
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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.
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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.
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Singh S, Chaudhary K, Dhanda SK, Bhalla S, Usmani SS, Gautam A, Tuknait A, Agrawal P, Mathur D, Raghava GPS. SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Res 2016; 44:D1119-26. [PMID: 26527728 PMCID: PMC4702810 DOI: 10.1093/nar/gkv1114] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 09/30/2015] [Accepted: 10/13/2015] [Indexed: 01/10/2023] Open
Abstract
SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.
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Affiliation(s)
- Sandeep Singh
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Kumardeep Chaudhary
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sandeep Kumar Dhanda
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sherry Bhalla
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | | | - Ankur Gautam
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Abhishek Tuknait
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Piyush Agrawal
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Deepika Mathur
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P S Raghava
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
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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.
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Dönitz J, Wingender E. EndoNet: an information resource about the intercellular signaling network. BMC SYSTEMS BIOLOGY 2014; 8:49. [PMID: 24758335 PMCID: PMC4017807 DOI: 10.1186/1752-0509-8-49] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2013] [Accepted: 04/17/2014] [Indexed: 12/02/2022]
Abstract
BACKGROUND In multicellular organisms, an intercellular signaling network communicates information from the environment or distant tissues to defined target cells. Intercellular signaling (mostly mediated by hormones) can affect the metabolic state and the gene expression program of target cells, thereby coordinating development, homeostasis of the organism and its reactions to external stimuli. Knowledge of the components of the intercellular signaling (specifically: the endocrine) network and their relations is an important, though so far a largely neglected part of systems biology. DESCRIPTION EndoNet is an information resource about the endocrine system in human. The content of this database comprises information about the biological components of the endocrine system, like hormones, receptors and cells, as well as their relations like the secretion or the binding of a hormone to its receptor. All data within EndoNet have been manually annotated from the scientific literature. The web interface of EndoNet provides the content by a detailed page for each component. These pages list information about the component, links to external resources including literature as well as to related entities of EndoNet. The anatomical ontology Cytomer is used, in conjunction with the Ontology Based Answers service (OBA), to query and list related anatomical structures ranging from the level of individual cells to complete organs. While querying the web interface the user can add components to an individual network. This network, or the complete network stored in the database, can be further analyzed in a configurable pipeline or can be exported in various formats. CONCLUSION EndoNet is an important and unique information resource about the intercellular signaling network. Since the intercellular network is an integral part of systems biology, EndoNet provides essential information for analyzing interaction between different cellular networks.
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Affiliation(s)
- Jürgen Dönitz
- Institute of Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Edgar Wingender
- Institute of Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
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Lyne M, Smith RN, Lyne R, Aleksic J, Hu F, Kalderimis A, Stepan R, Micklem G. metabolicMine: an integrated genomics, genetics and proteomics data warehouse for common metabolic disease research. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat060. [PMID: 23935057 PMCID: PMC4438919 DOI: 10.1093/database/bat060] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Common metabolic and endocrine diseases such as diabetes affect millions of people worldwide and have a major health impact, frequently leading to complications and mortality. In a search for better prevention and treatment, there is ongoing research into the underlying molecular and genetic bases of these complex human diseases, as well as into the links with risk factors such as obesity. Although an increasing number of relevant genomic and proteomic data sets have become available, the quantity and diversity of the data make their efficient exploitation challenging. Here, we present metabolicMine, a data warehouse with a specific focus on the genomics, genetics and proteomics of common metabolic diseases. Developed in collaboration with leading UK metabolic disease groups, metabolicMine integrates data sets from a range of experiments and model organisms alongside tools for exploring them. The current version brings together information covering genes, proteins, orthologues, interactions, gene expression, pathways, ontologies, diseases, genome-wide association studies and single nucleotide polymorphisms. Although the emphasis is on human data, key data sets from mouse and rat are included. These are complemented by interoperation with the RatMine rat genomics database, with a corresponding mouse version under development by the Mouse Genome Informatics (MGI) group. The web interface contains a number of features including keyword search, a library of Search Forms, the QueryBuilder and list analysis tools. This provides researchers with many different ways to analyse, view and flexibly export data. Programming interfaces and automatic code generation in several languages are supported, and many of the features of the web interface are available through web services. The combination of diverse data sets integrated with analysis tools and a powerful query system makes metabolicMine a valuable research resource. The web interface makes it accessible to first-time users, whereas the Application Programming Interface (API) and web services provide convenient data access and tools for bioinformaticians. metabolicMine is freely available online at http://www.metabolicmine.org Database URL: http://www.metabolicmine.org.
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Affiliation(s)
- Mike Lyne
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1QR, UK
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Kakourou G, Jaroudi S, Tulay P, Heath C, Serhal P, Harper JC, Sengupta SB. Investigation of gene expression profiles before and after embryonic genome activation and assessment of functional pathways at the human metaphase II oocyte and blastocyst stage. Fertil Steril 2012; 99:803-814.e23. [PMID: 23148922 DOI: 10.1016/j.fertnstert.2012.10.036] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Revised: 10/17/2012] [Accepted: 10/23/2012] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To compare the oocyte versus the blastocyst transcriptome and provide data on molecular pathways before and after embryonic genome activation. DESIGN Prospective laboratory research study. SETTING An IVF clinic and a specialist preimplantation genetics laboratory. PATIENT(S) Couples undergoing or having completed IVF treatment donating surplus oocytes or cryopreserved blastocysts after patient consent. INTERVENTION(S) Sets of pooled metaphase II (MII) oocytes or blastocysts were processed for RNA extraction, RNA amplification, and analysis with the use of the Human Genome Survey Microarrays v2.0 (Applied Biosystems). MAIN OUTCOME MEASURE(S) Association of cell type and gene expression profile. RESULT(S) Totals of 1,909 and 3,122 genes were uniquely expressed in human MII oocytes and human blastocysts respectively, and 4,910 genes were differentially expressed between the two sample types. Expression levels of 560 housekeeping genes, genes involved in the microRNA processing pathway, as well as hormones and hormone receptors were also investigated. CONCLUSION(S) The lists of genes identified may be of use for understanding the processes involved in early embryo development and blastocyst implantation, and for identifying any dysregulation leading to infertility.
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Affiliation(s)
- Georgia Kakourou
- UCL Centre for Preimplantation Genetic Diagnosis, Institute for Women's Health, University College London, London, United Kingdom.
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Abstract
BACKGROUND Cancer is responsible for millions of immature deaths every year and is an economical burden on developing countries. One of the major challenges in the present era is to design drugs that can specifically target tumor cells not normal cells. In this context, tumor homing peptides have drawn much attention. These peptides are playing a vital role in delivering drugs in tumor tissues with high specificity. In order to provide service to scientific community, we have developed a database of tumor homing peptides called TumorHoPe. DESCRIPTION TumorHoPe is a manually curated database of experimentally validated tumor homing peptides that specifically recognize tumor cells and tumor associated microenvironment, i.e., angiogenesis. These peptides were collected and compiled from published papers, patents and databases. Current release of TumorHoPe contains 744 peptides. Each entry provides comprehensive information of a peptide that includes its sequence, target tumor, target cell, techniques of identification, peptide receptor, etc. In addition, we have derived various types of information from these peptide sequences that include secondary/tertiary structure, amino acid composition, and physicochemical properties of peptides. Peptides in this database have been found to target different types of tumors that include breast, lung, prostate, melanoma, colon, etc. These peptides have some common motifs including RGD (Arg-Gly-Asp) and NGR (Asn-Gly-Arg) motifs, which specifically recognize tumor angiogenic markers. TumorHoPe has been integrated with many web-based tools like simple/complex search, database browsing and peptide mapping. These tools allow a user to search tumor homing peptides based on their amino acid composition, charge, polarity, hydrophobicity, etc. CONCLUSION TumorHoPe is a unique database of its kind, which provides comprehensive information about experimentally validated tumor homing peptides and their target cells. This database will be very useful in designing peptide-based drugs and drug-delivery system. It is freely available at http://crdd.osdd.net/raghava/tumorhope/.
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Jiang YY, Kong DX, Qin T, Zhang HY. How does oxygen rise drive evolution? Clues from oxygen-dependent biosynthesis of nuclear receptor ligands. Biochem Biophys Res Commun 2010; 391:1158-60. [DOI: 10.1016/j.bbrc.2009.11.041] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2009] [Accepted: 11/05/2009] [Indexed: 11/29/2022]
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Vanhee P, Reumers J, Stricher F, Baeten L, Serrano L, Schymkowitz J, Rousseau F. PepX: a structural database of non-redundant protein-peptide complexes. Nucleic Acids Res 2009; 38:D545-51. [PMID: 19880386 PMCID: PMC2808939 DOI: 10.1093/nar/gkp893] [Citation(s) in RCA: 79] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Although protein–peptide interactions are estimated to constitute up to 40% of all protein interactions, relatively little information is available for the structural details of these interactions. Peptide-mediated interactions are a prime target for drug design because they are predominantly present in signaling and regulatory networks. A reliable data set of nonredundant protein–peptide complexes is indispensable as a basis for modeling and design, but current data sets for protein–peptide interactions are often biased towards specific types of interactions or are limited to interactions with small ligands. In PepX (http://pepx.switchlab.org), we have designed an unbiased and exhaustive data set of all protein–peptide complexes available in the Protein Data Bank with peptide lengths up to 35 residues. In addition, these complexes have been clustered based on their binding interfaces rather than sequence homology, providing a set of structurally diverse protein–peptide interactions. The final data set contains 505 unique protein–peptide interface clusters from 1431 complexes. Thorough annotation of each complex with both biological and structural information facilitates searching for and browsing through individual complexes and clusters. Moreover, we provide an additional source of data for peptide design by annotating peptides with naturally occurring backbone variations using fragment clusters from the BriX database.
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Affiliation(s)
- Peter Vanhee
- VIB SWITCH Laboratory, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
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