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Umavathy K, Sankar P. Ontology based conceptual models for predicting fundamental organic reactivity. J Mol Graph Model 2020; 100:107691. [DOI: 10.1016/j.jmgm.2020.107691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/18/2020] [Accepted: 07/08/2020] [Indexed: 10/23/2022]
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2
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Kwon S, Yoon S. End-to-End Representation Learning for Chemical-Chemical Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1436-1447. [PMID: 30106687 DOI: 10.1109/tcbb.2018.2864149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. In addition, DeepCCI was experimentally validated to guarantee the commutative property. The automatically extracted features can alleviate the efforts required for manual feature engineering and improve prediction performance.
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3
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Vinoth P, Sankar P. Encoding of coordination complexes with XML. J Mol Graph Model 2017; 76:242-259. [PMID: 28735171 DOI: 10.1016/j.jmgm.2017.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 07/08/2017] [Accepted: 07/10/2017] [Indexed: 11/29/2022]
Abstract
An in-silico system to encode structure, bonding and properties of coordination complexes is developed. The encoding is achieved through a semantic XML markup frame. Composition of the coordination complexes is captured in terms of central atom and ligands. Structural information of central atom is detailed in terms of electron status of valence electron orbitals. The ligands are encoded with specific reference to the electron environment of ligand centre atoms. Behaviour of ligands to form low or high spin complexes is accomplished by assigning a Ligand Centre Value to every ligand based on the electronic environment of ligand centre atom. Chemical ontologies are used for categorization purpose and to control different hybridization schemes. Complexes formed by the central atoms of transition metal, non-transition elements belonging to s-block, p-block and f-block are encoded with a generic encoding platform. Complexes of homoleptic, heteroleptic and bridged types are also covered by this encoding system. Utility of the encoded system to predict redox electron transfer reaction in the coordination complexes is demonstrated with a simple application.
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Affiliation(s)
- P Vinoth
- Department of Chemistry, Pondicherry Engineering College, Puducherry 605 014, India
| | - P Sankar
- Department of Chemistry, Pondicherry Engineering College, Puducherry 605 014, India.
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Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease. Aging (Albany NY) 2016; 7:616-28. [PMID: 26342919 PMCID: PMC4600621 DOI: 10.18632/aging.100799] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
As the level of interest in aging research increases, there is a growing number of geroprotectors, or therapeutic interventions that aim to extend the healthy lifespan and repair or reduce aging-related damage in model organisms and, eventually, in humans. There is a clear need for a manually-curated database of geroprotectors to compile and index their effects on aging and age-related diseases and link these effects to relevant studies and multiple biochemical and drug databases. Here, we introduce the first such resource, Geroprotectors (http://geroprotectors.org). Geroprotectors is a public, rapidly explorable database that catalogs over 250 experiments involving over 200 known or candidate geroprotectors that extend lifespan in model organisms. Each compound has a comprehensive profile complete with biochemistry, mechanisms, and lifespan effects in various model organisms, along with information ranging from chemical structure, side effects, and toxicity to FDA drug status. These are presented in a visually intuitive, efficient framework fit for casual browsing or in-depth research alike. Data are linked to the source studies or databases, providing quick and convenient access to original data. The Geroprotectors database facilitates cross-study, cross-organism, and cross-discipline analysis and saves countless hours of inefficient literature and web searching. Geroprotectors is a one-stop, knowledge-sharing, time-saving resource for researchers seeking healthy aging solutions.
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Sánchez BJ, Nielsen J. Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr Biol (Camb) 2016; 7:846-58. [PMID: 26079294 DOI: 10.1039/c5ib00083a] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.
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Affiliation(s)
- Benjamín J Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden.
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Encoding of Fundamental Chemical Entities of Organic Reactivity Interest using chemical ontology and XML. J Mol Graph Model 2015; 61:30-43. [DOI: 10.1016/j.jmgm.2015.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 04/16/2015] [Accepted: 06/09/2015] [Indexed: 11/17/2022]
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Shilpi A, Parbin S, Sengupta D, Kar S, Deb M, Rath SK, Pradhan N, Rakshit M, Patra SK. Mechanisms of DNA methyltransferase-inhibitor interactions: Procyanidin B2 shows new promise for therapeutic intervention of cancer. Chem Biol Interact 2015; 233:122-38. [PMID: 25839702 DOI: 10.1016/j.cbi.2015.03.022] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2014] [Revised: 03/17/2015] [Accepted: 03/22/2015] [Indexed: 12/20/2022]
Abstract
DNA methyltransferases (DNMTs) is a key epigenetic enzyme for pharmacological manipulation and is employed in cancer reprogramming. During past few years multiple strategies have been implemented to excavate epigenetic compounds targeting DNMTs. In light of the emerging concept of chemoinformatics, molecular docking and simulation studies have been employed to accelerate the development of DNMT inhibitors. Among the DNMT inhibitors known till date, epigallocathechin-3-gallate (EGCG) was identified to be effective in reducing DNMT activity. However, the broad spectrum of EGCG to other diseases and variable target enzymes offers some limitations. In view of this, 32 EGCG analogues were screened at S-Adnosyl-L-homocysteine (SAH) binding pocket of DNMTs and procyanidin B2-3, 3'-di-O-gallate (procyanidin B2) was obtained as potent inhibitor having medicinally relevant chemical space. Further, in vitro analysis demonstrates the efficiency of procyanidin B2 in attenuating DNMT activity at IC50 of 6.88±0.647 μM and subsequently enhancing the expression of DNMT target genes, E-cadherin, Maspin and BRCA1. Moreover, the toxic property of procyanidin B2 towards triple negative breast cancer cells to normal cells offers platform for pre-clinical trial and an insight to the treatment of cancer.
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Affiliation(s)
- Arunima Shilpi
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Sabnam Parbin
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Dipta Sengupta
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Swayamsiddha Kar
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Moonmoon Deb
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Sandip Kumar Rath
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Nibedita Pradhan
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Madhumita Rakshit
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India
| | - Samir Kumar Patra
- Epigenetics and Cancer Research Laboratory, Biochemistry and Molecular Biology Group, Department of Life Science, National Institute of Technology, Rourkela, Odisha 769008, India.
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Rienksma RA, Suarez-Diez M, Spina L, Schaap PJ, Martins dos Santos VAP. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets. Semin Immunol 2014; 26:610-22. [PMID: 25453232 DOI: 10.1016/j.smim.2014.09.013] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 09/26/2014] [Accepted: 09/29/2014] [Indexed: 12/28/2022]
Abstract
Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.
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MESH Headings
- Antitubercular Agents/therapeutic use
- Bacterial Proteins/genetics
- Bacterial Proteins/metabolism
- Carbon Isotopes
- Drug Resistance, Multiple, Bacterial/genetics
- Gene Expression Regulation, Bacterial
- Gene Regulatory Networks
- Genome, Bacterial
- Host-Pathogen Interactions
- Humans
- Metabolic Networks and Pathways/genetics
- Models, Statistical
- Molecular Targeted Therapy
- Mycobacterium tuberculosis/drug effects
- Mycobacterium tuberculosis/genetics
- Mycobacterium tuberculosis/metabolism
- Systems Biology
- Tuberculosis, Multidrug-Resistant/drug therapy
- Tuberculosis, Multidrug-Resistant/metabolism
- Tuberculosis, Multidrug-Resistant/microbiology
- Tuberculosis, Multidrug-Resistant/pathology
- Tuberculosis, Pulmonary/drug therapy
- Tuberculosis, Pulmonary/metabolism
- Tuberculosis, Pulmonary/microbiology
- Tuberculosis, Pulmonary/pathology
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Affiliation(s)
- Rienk A Rienksma
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Lucie Spina
- Centre National de la Rescherche Scientifique (CNRS), Institut de Pharmacologie et de Biologie Structurale (UMR 5089), Department of Tuberculosis and Infection Biology and Université de Toulouse (Université Paul Sabatier, Toulouse III), IPBS, 205 Route de Narbonne, BP 64182, F-31077 Toulouse, France
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Vitor A P Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands; Lifeglimmer GmbH, Markelstrasse 38, 12163 Berlin, Germany.
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de Souza A, Bittker JA, Lahr DL, Brudz S, Chatwin S, Oprea TI, Waller A, Yang JJ, Southall N, Guha R, Schürer SC, Vempati UD, Southern MR, Dawson ES, Clemons PA, Chung TDY. An Overview of the Challenges in Designing, Integrating, and Delivering BARD: A Public Chemical-Biology Resource and Query Portal for Multiple Organizations, Locations, and Disciplines. ACTA ACUST UNITED AC 2014; 19:614-27. [PMID: 24441647 DOI: 10.1177/1087057113517139] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 11/22/2013] [Indexed: 01/15/2023]
Abstract
Recent industry-academic partnerships involve collaboration among disciplines, locations, and organizations using publicly funded "open-access" and proprietary commercial data sources. These require the effective integration of chemical and biological information from diverse data sources, which presents key informatics, personnel, and organizational challenges. The BioAssay Research Database (BARD) was conceived to address these challenges and serve as a community-wide resource and intuitive web portal for public-sector chemical-biology data. Its initial focus is to enable scientists to more effectively use the National Institutes of Health Roadmap Molecular Libraries Program (MLP) data generated from the 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), which is currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage BioAssay Ontology and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the interdisciplinary BARD team, veterans of public- and private-sector data-integration projects, who are collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution.
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Affiliation(s)
| | | | - David L Lahr
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Steve Brudz
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Simon Chatwin
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Tudor I Oprea
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Anna Waller
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Jeremy J Yang
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Noel Southall
- NIH Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Rajarshi Guha
- NIH Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Stephan C Schürer
- Center for Computational Science, University of Miami, Miami, FL, USA
| | - Uma D Vempati
- Center for Computational Science, University of Miami, Miami, FL, USA
| | - Mark R Southern
- The Translational Research Institute, The Scripps Research Institute, Jupiter, FL, USA
| | - Eric S Dawson
- The Vanderbilt Specialized Chemistry Center for Accelerated Probe Development, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Thomas D Y Chung
- Conrad Prebys Center for Chemical Genomics, Sanford
- Burnham Medical Research Institute, La Jolla, CA, USA
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Sankar P, Krief A, Vijayasarathi D. A conceptual basis to encode and detect organic functional groups in XML. J Mol Graph Model 2013; 43:1-10. [PMID: 23666030 DOI: 10.1016/j.jmgm.2013.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Revised: 04/06/2013] [Accepted: 04/13/2013] [Indexed: 10/26/2022]
Abstract
A conceptual basis to define and detect organic functional groups is developed. The basic model of a functional group is termed as a primary functional group and is characterized by a group center composed of one or more group center atoms bonded to terminal atoms and skeletal carbon atoms. The generic group center patterns are identified from the structures of known functional groups. Accordingly, a chemical ontology 'Font' is developed to organize the existing functional groups as well as the new ones to be defined by the chemists. The basic model is extended to accommodate various combinations of primary functional groups as functional group assemblies. A concept of skeletal group is proposed to define the characteristic groups composed of only carbon atoms to be regarded as equivalent to functional groups. The combination of primary functional groups with skeletal groups is categorized as skeletal group assembly. In order to make the model suitable for reaction modeling purpose, a Graphical User Interface (GUI) is developed to define the functional groups and to encode in XML format appropriate to detect them in chemical structures. The system is capable of detecting multiple instances of primary functional groups as well as the overlapping poly-functional groups as the respective assemblies.
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Affiliation(s)
- Punnaivanam Sankar
- Department of Chemistry, Pondicherry Engineering College, Puducherry 605014, India.
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11
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Cheng C, Shaw N, Zhang X, Zhang M, Ding W, Wang BC, Liu ZJ. Structural view of a non Pfam singleton and crystal packing analysis. PLoS One 2012; 7:e31673. [PMID: 22363703 PMCID: PMC3282739 DOI: 10.1371/journal.pone.0031673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2011] [Accepted: 01/11/2012] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Comparative genomic analysis has revealed that in each genome a large number of open reading frames have no homologues in other species. Such singleton genes have attracted the attention of biochemists and structural biologists as a potential untapped source of new folds. Cthe_2751 is a 15.8 kDa singleton from an anaerobic, hyperthermophile Clostridium thermocellum. To gain insights into the architecture of the protein and obtain clues about its function, we decided to solve the structure of Cthe_2751. RESULTS The protein crystallized in 4 different space groups that diffracted X-rays to 2.37 Å (P3(1)21), 2.17 Å (P2(1)2(1)2(1)), 3.01 Å (P4(1)22), and 2.03 Å (C222(1)) resolution, respectively. Crystal packing analysis revealed that the 3-D packing of Cthe_2751 dimers in P4(1)22 and C222(1) is similar with only a rotational difference of 2.69° around the C axes. A new method developed to quantify the differences in packing of dimers in crystals from different space groups corroborated the findings of crystal packing analysis. Cthe_2751 is an all α-helical protein with a central hydrophobic core providing thermal stability via π:cation and π: π interactions. A ProFunc analysis retrieved a very low match with a splicing endonuclease, suggesting a role for the protein in the processing of nucleic acids. CONCLUSIONS Non-Pfam singleton Cthe_2751 folds into a known all α-helical fold. The structure has increased sequence coverage of non-Pfam proteins such that more protein sequences can be amenable to modelling. Our work on crystal packing analysis provides a new method to analyze dimers of the protein crystallized in different space groups. The utility of such an analysis can be expanded to oligomeric structures of other proteins, especially receptors and signaling molecules, many of which are known to function as oligomers.
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Affiliation(s)
- Chongyun Cheng
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Neil Shaw
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- College of Stem Cell and Molecular Clinical Medicine, Kunming Medical University, Kunming, China
| | - Xuejun Zhang
- Department of Immunology, Tianjin Medical University, Tianjin, China
| | - Min Zhang
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Wei Ding
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Bi-Cheng Wang
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, United States of America
| | - Zhi-Jie Liu
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- College of Stem Cell and Molecular Clinical Medicine, Kunming Medical University, Kunming, China
- * E-mail:
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Chen H, Engkvist O, Blomberg N, Li J. A comparative analysis of the molecular topologies for drugs, clinical candidates, natural products, human metabolites and general bioactive compounds. MEDCHEMCOMM 2012. [DOI: 10.1039/c2md00238h] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Kuchta K, Barszcz D, Grzesiuk E, Pomorski P, Krwawicz J. DNAtraffic--a new database for systems biology of DNA dynamics during the cell life. Nucleic Acids Res 2011; 40:D1235-40. [PMID: 22110027 PMCID: PMC3245060 DOI: 10.1093/nar/gkr962] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications.
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Affiliation(s)
- Krzysztof Kuchta
- Department of Molecular Biology, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
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14
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Yang L, Wang KJ, Wang LS, Jegga AG, Qin SY, He G, Chen J, Xiao Y, He L. Chemical-protein interactome and its application in off-target identification. Interdiscip Sci 2011; 3:22-30. [PMID: 21369884 DOI: 10.1007/s12539-011-0051-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2010] [Revised: 09/14/2010] [Accepted: 09/19/2010] [Indexed: 01/30/2023]
Abstract
Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.
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Affiliation(s)
- Lun Yang
- Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.
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Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011. [PMID: 21059682 PMCID: PMC3013709 DOI: 10.1093/nar/gkq1126+10.1053/j.gastro.2017.02.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data 'depth'). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug-drug and food-drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of 'omics' (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.
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Affiliation(s)
- Craig Knox
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
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Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011; 39:D1035-D1041. [PMID: 21059682 PMCID: PMC3013709 DOI: 10.1093/nar/gkq1126 10.1053/j.gastro.2017.02.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2010] [Revised: 10/20/2010] [Accepted: 10/21/2010] [Indexed: 01/31/2024] Open
Abstract
DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data 'depth'). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug-drug and food-drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of 'omics' (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.
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Affiliation(s)
- Craig Knox
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Vivian Law
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Timothy Jewison
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Philip Liu
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Son Ly
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Alex Frolkis
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Allison Pon
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Kelly Banco
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Christine Mak
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Vanessa Neveu
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Yannick Djoumbou
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - Roman Eisner
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - An Chi Guo
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
| | - David S. Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8, Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2N8, Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8 and National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
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Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011. [PMID: 21059682 DOI: 10.1093/nargkq1126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data 'depth'). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug-drug and food-drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of 'omics' (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.
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Affiliation(s)
- Craig Knox
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
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Sreenivasaiah PK, Kim DH. Current trends and new challenges of databases and web applications for systems driven biological research. Front Physiol 2010; 1:147. [PMID: 21423387 PMCID: PMC3059952 DOI: 10.3389/fphys.2010.00147] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2010] [Accepted: 10/18/2010] [Indexed: 12/17/2022] Open
Abstract
Dynamic and rapidly evolving nature of systems driven research imposes special requirements on the technology, approach, design and architecture of computational infrastructure including database and Web application. Several solutions have been proposed to meet the expectations and novel methods have been developed to address the persisting problems of data integration. It is important for researchers to understand different technologies and approaches. Having familiarized with the pros and cons of the existing technologies, researchers can exploit its capabilities to the maximum potential for integrating data. In this review we discuss the architecture, design and key technologies underlying some of the prominent databases and Web applications. We will mention their roles in integration of biological data and investigate some of the emerging design concepts and computational technologies that are likely to have a key role in the future of systems driven biomedical research.
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Affiliation(s)
- Pradeep Kumar Sreenivasaiah
- Systems Biology Research Center and College of Life Science, Gwangju Institute of Science and TechnologyGwangju, Republic of Korea
| | - Do Han Kim
- Systems Biology Research Center and College of Life Science, Gwangju Institute of Science and TechnologyGwangju, Republic of Korea
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Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2010; 39:D1035-41. [PMID: 21059682 PMCID: PMC3013709 DOI: 10.1093/nar/gkq1126] [Citation(s) in RCA: 1278] [Impact Index Per Article: 91.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.
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Affiliation(s)
- Craig Knox
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
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Smallbone K, Simeonidis E, Swainston N, Mendes P. Towards a genome-scale kinetic model of cellular metabolism. BMC SYSTEMS BIOLOGY 2010; 4:6. [PMID: 20109182 PMCID: PMC2829494 DOI: 10.1186/1752-0509-4-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2009] [Accepted: 01/28/2010] [Indexed: 11/10/2022]
Abstract
Background Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour. Results We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system. Conclusions Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.
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Affiliation(s)
- Kieran Smallbone
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK
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22
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A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 2010; 5:93-121. [PMID: 20057383 DOI: 10.1038/nprot.2009.203] [Citation(s) in RCA: 1118] [Impact Index Per Article: 79.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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Macchiarulo A, Thornton JM, Nobeli I. Mapping human metabolic pathways in the small molecule chemical space. J Chem Inf Model 2009; 49:2272-89. [PMID: 19795883 DOI: 10.1021/ci900196u] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The work presented here is a study of human metabolic pathways, as projected in the chemical space of the small molecules they comprise, and it is composed of three parts: a) a study of the extent of clustering and overlap of these pathways in chemical space, b) the development and assessment of a statistical model for estimating the proximity to a given pathway of any small molecule, and c) the use of the above model in estimating the proximity of marketed drugs to human metabolic pathways. The distribution, overlap, and relationships of human metabolic pathways in this space are revealed using both visual and quantitative approaches. A set of selected physicochemical and topological descriptors is used to build a classifier, whose aim is to predict metabolic class and pathway membership of any small molecule. The classifier performs well for tightly clustered, isolated pathways but is, naturally, much less accurate for strongly overlapping pathways. Finally, the extent of overlap of a set of known drugs with the human metabolome is examined, and the classifier is used to predict likely cross-interactions between drugs and the major metabolic pathways in humans.
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Affiliation(s)
- Antonio Macchiarulo
- Dip. Chimica e Tecnologia del Farmaco, Faculty of Pharmacy, University of Perugia, Via del Liceo 1, 06123 Perugia, Italy.
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Forsythe IJ, Wishart DS. Exploring human metabolites using the human metabolome database. ACTA ACUST UNITED AC 2009; Chapter 14:14.8.1-14.8.45. [PMID: 19274632 DOI: 10.1002/0471250953.bi1408s25] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The Human Metabolome Database (HMDB) is a Web-based bioinformatic/cheminformatic resource with detailed information about human metabolites and metabolic enzymes. It can be used for fields of study including metabolomics, biochemistry, clinical chemistry, biomarker discovery, medicine, nutrition, and general education. In addition to its comprehensive literature-derived data, the HMDB contains an extensive collection of experimental metabolite concentration data for plasma, urine, CSF, and/or other biofluids The HMDB is fully searchable, with many tools for viewing, sorting and extracting metabolite names, chemical structures, biofluid concentrations, enzymes, genes, NMR or MS spectra, and disease information. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, chemical structure information, physico-chemical data, reference NMR and MS spectra, normal and abnormal biofluid concentrations, tissue locations, disease associations, pathway information, enzyme data, gene sequence data, and SNP and mutation data, as well as extensive links to images, references and other public databases.
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Affiliation(s)
- Ian J Forsythe
- Genome Alberta, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
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Niu B, Jin Y, Lu L, Fen K, Gu L, He Z, Lu W, Li Y, Cai Y. Prediction of interaction between small molecule and enzyme using AdaBoost. Mol Divers 2009; 13:313-20. [PMID: 19219560 DOI: 10.1007/s11030-009-9116-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2008] [Accepted: 01/17/2009] [Indexed: 11/30/2022]
Abstract
The knowledge of whether one enzyme can interact with a small molecule is essential for understanding the molecular and cellular functions of organisms. In this paper, we introduce a classifier to predict the small molecule- enzyme interaction, i.e., whether they can interact with each other. Small molecules are represented by their chemical functional groups, and enzymes are represented by their biochemical and physicochemical properties, resulting in a total of 160 features. These features are input into the AdaBoost classifier, which is known to have good generalization ability to predict interaction. As a result, the overall prediction accuracy, tested by tenfold cross-validation and independent sets, is 81.76% and 83.35%, respectively, suggesting that this strategy is effective. In this research, we typically choose interactions between small molecules and enzymes involved in metabolism to ultimately improve further understanding of metabolic pathways. An online predictor developed by this research is available at http://chemdata.shu.edu.cn/small_m .
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Affiliation(s)
- Bing Niu
- Shanghai University, Shanghai 200072, China
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Abstract
WikiGenes is the first wiki system to combine the collaborative and largely altruistic possibilities of wikis with explicit authorship. In view of the extraordinary success of Wikipedia there remains no doubt about the potential of collaborative publishing, yet its adoption in science has been limited. Here I discuss a dynamic collaborative knowledge base for the life sciences that provides authors with due credit and that can evolve via continual revision and traditional peer review into a rigorous scientific tool.
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Affiliation(s)
- Robert Hoffmann
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, Massachusetts 02139, USA.
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27
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Kell DB. Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases. BMC Med Genomics 2009; 2:2. [PMID: 19133145 PMCID: PMC2672098 DOI: 10.1186/1755-8794-2-2] [Citation(s) in RCA: 364] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2008] [Accepted: 01/08/2009] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular 'reactive oxygen species' (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. REVIEW We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation).The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible.This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, since in some circumstances (especially the presence of poorly liganded iron) molecules that are nominally antioxidants can actually act as pro-oxidants. The reduction of redox stress thus requires suitable levels of both antioxidants and effective iron chelators. Some polyphenolic antioxidants may serve both roles.Understanding the exact speciation and liganding of iron in all its states is thus crucial to separating its various pro- and anti-inflammatory activities. Redox stress, innate immunity and pro- (and some anti-)inflammatory cytokines are linked in particular via signalling pathways involving NF-kappaB and p38, with the oxidative roles of iron here seemingly involved upstream of the IkappaB kinase (IKK) reaction. In a number of cases it is possible to identify mechanisms by which ROSs and poorly liganded iron act synergistically and autocatalytically, leading to 'runaway' reactions that are hard to control unless one tackles multiple sites of action simultaneously. Some molecules such as statins and erythropoietin, not traditionally associated with anti-inflammatory activity, do indeed have 'pleiotropic' anti-inflammatory effects that may be of benefit here. CONCLUSION Overall we argue, by synthesising a widely dispersed literature, that the role of poorly liganded iron has been rather underappreciated in the past, and that in combination with peroxide and superoxide its activity underpins the behaviour of a great many physiological processes that degrade over time. Understanding these requires an integrative, systems-level approach that may lead to novel therapeutic targets.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess St, Manchester, M1 7DN, UK.
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28
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Dobson PD, Patel Y, Kell DB. ‘Metabolite-likeness’ as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today 2009; 14:31-40. [DOI: 10.1016/j.drudis.2008.10.011] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2008] [Revised: 10/14/2008] [Accepted: 10/21/2008] [Indexed: 10/21/2022]
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A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008; 26:1155-60. [PMID: 18846089 DOI: 10.1038/nbt1492] [Citation(s) in RCA: 405] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.
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McShan DC. Towards inference of a biochemical ontology from a metabolic database. Comp Funct Genomics 2008; 6:398-406. [PMID: 18629198 PMCID: PMC2447492 DOI: 10.1002/cfg.500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2005] [Revised: 12/01/2005] [Accepted: 12/05/2005] [Indexed: 11/24/2022] Open
Abstract
In order to predict the metabolic fate of an arbitrary compound based solely on
structure, it is useful to be able to identify substructural ‘functional groups’ that are
biochemically reactive. These functional groups are the substructural elements that
can be removed and replaced to transform one compound into another. This problem
of identifying functional groups is related to the problem of classifying compounds.
The research presented here discusses the state of the art in biochemical databases
and how these sources may be applied to the problem of classifying compounds based
solely on structure. We describe a biochemical informatics system for processing
molecular data and describe how 100 255 compositional (hasA) relationships are
inferred between 835 abstractions and 9500 metabolites from the KEGG Ligand
database. Specifically, we focus on the identification of amino acids and consider ways
in which the inference of biochemical ontologies for metabolites will be improved in
the future.
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Affiliation(s)
- D C McShan
- School of Medicine University of Colorado Health Sciences Center PO Box 6511 Mail stop 8303 Aurora CO 80045-0511 USA
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31
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Petri Seiler K, Kuehn H, Pat Happ M, Decaprio D, Clemons PA. Using ChemBank to probe chemical biology. ACTA ACUST UNITED AC 2008; Chapter 14:14.5.1-14.5.26. [PMID: 18551413 DOI: 10.1002/0471250953.bi1405s22] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ChemBank (http://chembank.broad.harvard.edu/) is a public, Web-based informatics environment. ChemBank stores and makes freely available data derived from small molecules and small-molecule screens and has resources for relating and studying these data. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays performed at the Broad Institute screening center. Web-based analysis tools are available within ChemBank to study the relationships between small molecules, cell measurements, and cell states. This unit demonstrates the use of ChemBank data to ask and answer questions relating to chemical biology and screening experiments contained within ChemBank.
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Affiliation(s)
- Kathleen Petri Seiler
- Chemical Biology Program and Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Wishart DS. Introduction to cheminformatics. CURRENT PROTOCOLS IN BIOINFORMATICS 2008; Chapter 14:Unit 14.1. [PMID: 18428788 DOI: 10.1002/0471250953.bi1401s18] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Cheminformatics is a relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. The chemical data of interest typically includes information on small molecule formulas, structures, properties, spectra, and activities (biological or industrial). Cheminformatics originally emerged as a vehicle to help the drug discovery and development process, however cheminformatics now plays an increasingly important role in many areas of biology, chemistry, and biochemistry. The intent of this unit is to give readers some introduction into the field of cheminformatics and to show how cheminformatics not only shares many similarities with the field of bioinformatics, but that it can also enhance much of what is currently done in bioinformatics.
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Adams N, Murray-Rust P. Engineering Polymer Informatics: Towards the Computer-Aided Design of Polymers. Macromol Rapid Commun 2008. [DOI: 10.1002/marc.200700832] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Ma H, Goryanin I. Human metabolic network reconstruction and its impact on drug discovery and development. Drug Discov Today 2008; 13:402-8. [PMID: 18468557 DOI: 10.1016/j.drudis.2008.02.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2007] [Revised: 01/23/2008] [Accepted: 02/06/2008] [Indexed: 12/20/2022]
Abstract
This paper describes the process for the reconstruction of a high quality human metabolic network from the genome information, the existing problems in the reconstruction and why a time-consuming literature based consolidation process is needed. The reconstructed metabolic network provides a unified platform to integrate all the biological and medical information on genes, proteins, metabolites, disease, drugs and drug targets for a system level study of the relationship between metabolism and disease. System analysis of metabolic networks will help us, not only in identifying new drug targets but also in developing a system-oriented drug design strategy.
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Affiliation(s)
- Hongwu Ma
- School of Informatics, the University of Edinburgh, Mayfield Road, Edinburgh, United Kingdom
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35
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Taswell C. DOORS to the Semantic Web and Grid With a PORTAL for Biomedical Computing. ACTA ACUST UNITED AC 2008; 12:191-204. [DOI: 10.1109/titb.2007.905861] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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36
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Kuhn M, Campillos M, González P, Jensen LJ, Bork P. Large-scale prediction of drug-target relationships. FEBS Lett 2008; 582:1283-90. [DOI: 10.1016/j.febslet.2008.02.024] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2007] [Revised: 02/08/2008] [Accepted: 02/11/2008] [Indexed: 10/22/2022]
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37
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Senger S, Leach AR. SAR Knowledge Bases in Drug Discovery. ACTA ACUST UNITED AC 2008. [DOI: 10.1016/s1574-1400(08)00011-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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38
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Zhang C, Crasta O, Cammer S, Will R, Kenyon R, Sullivan D, Yu Q, Sun W, Jha R, Liu D, Xue T, Zhang Y, Moore M, McGarvey P, Huang H, Chen Y, Zhang J, Mazumder R, Wu C, Sobral B. An emerging cyberinfrastructure for biodefense pathogen and pathogen-host data. Nucleic Acids Res 2008; 36:D884-91. [PMID: 17984082 PMCID: PMC2239001 DOI: 10.1093/nar/gkm903] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2007] [Revised: 10/04/2007] [Accepted: 10/05/2007] [Indexed: 01/07/2023] Open
Abstract
The NIAID-funded Biodefense Proteomics Resource Center (RC) provides storage, dissemination, visualization and analysis capabilities for the experimental data deposited by seven Proteomics Research Centers (PRCs). The data and its publication is to support researchers working to discover candidates for the next generation of vaccines, therapeutics and diagnostics against NIAID's Category A, B and C priority pathogens. The data includes transcriptional profiles, protein profiles, protein structural data and host-pathogen protein interactions, in the context of the pathogen life cycle in vivo and in vitro. The database has stored and supported host or pathogen data derived from Bacillus, Brucella, Cryptosporidium, Salmonella, SARS, Toxoplasma, Vibrio and Yersinia, human tissue libraries, and mouse macrophages. These publicly available data cover diverse data types such as mass spectrometry, yeast two-hybrid (Y2H), gene expression profiles, X-ray and NMR determined protein structures and protein expression clones. The growing database covers over 23 000 unique genes/proteins from different experiments and organisms. All of the genes/proteins are annotated and integrated across experiments using UniProt Knowledgebase (UniProtKB) accession numbers. The web-interface for the database enables searching, querying and downloading at the level of experiment, group and individual gene(s)/protein(s) via UniProtKB accession numbers or protein function keywords. The system is accessible at http://www.proteomicsresource.org/.
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Affiliation(s)
- C. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - O. Crasta
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - S. Cammer
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Will
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Kenyon
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Sullivan
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Q. Yu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - W. Sun
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Jha
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - D. Liu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - T. Xue
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - M. Moore
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - P. McGarvey
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - H. Huang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - Y. Chen
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - J. Zhang
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - R. Mazumder
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - C. Wu
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
| | - B. Sobral
- Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University, Washington Street (0477), Blacksburg, VA 24061, Social & Scientific Systems, Inc., 8757 Georgia Avenue, 12th Floor Silver Spring, MD 20910 and Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
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Kuhn M, von Mering C, Campillos M, Jensen LJ, Bork P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 2007; 36:D684-8. [PMID: 18084021 PMCID: PMC2238848 DOI: 10.1093/nar/gkm795] [Citation(s) in RCA: 537] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/
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Affiliation(s)
- Michael Kuhn
- European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
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40
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Varadwaj PK, Lahiri T. FGO: a novel ontology for identification of ligand functional group. Bioinformation 2007; 2:113-8. [PMID: 18288335 PMCID: PMC2248449 DOI: 10.6026/97320630002113] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2007] [Revised: 11/03/2007] [Accepted: 11/06/2007] [Indexed: 11/23/2022] Open
Abstract
Small molecules play crucial role in the modulation of biological functions by interacting with specific macromolecules. Hence small molecule interactions are captured by a variety of experimental methods to estimate and propose correlations between molecular structures to their biological activities. The tremendous expanse in publicly available small molecules is also driving new efforts to better understand interactions involving small molecules particularly in area of drug docking and pharmacogenomics. We have studied and designed a functional group identification system with the associated ontology for it. The functional group identification system can detect the functional group components from given ligand structure with specific coordinate information. Functional group ontology (FGO) proposed by us is a structured classification of chemical functional group which acts as an important source of prior knowledge that may be automatically integrated to support identification, categorization and predictive data analysis tasks. We have used a new annotation method which can be used to construct the original structure from given ontological expression using exact coordinate information. Here, we also discuss about ontology-driven similarity measure of functional groups and uses of such novel ontology for pharmacophore searching and de-novo ligand designing.
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Affiliation(s)
| | - Tapobrata Lahiri
- Indian Institute of Information Technology, Allahabad, India - 211012
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41
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Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2007; 36:D901-6. [PMID: 18048412 PMCID: PMC2238889 DOI: 10.1093/nar/gkm958] [Citation(s) in RCA: 1760] [Impact Index Per Article: 103.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.
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Affiliation(s)
- David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8.
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42
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Faulon JL, Misra M, Martin S, Sale K, Sapra R. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. ACTA ACUST UNITED AC 2007; 24:225-33. [PMID: 18037612 DOI: 10.1093/bioinformatics/btm580] [Citation(s) in RCA: 114] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.
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Affiliation(s)
- Jean-Loup Faulon
- Sandia National Laboratories, Computational Biosciences Department, P.O. Box 5800, Albuquerque, NM 87185-1413, USA.
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Okuno Y, Tamon A, Yabuuchi H, Niijima S, Minowa Y, Tonomura K, Kunimoto R, Feng C. GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update. Nucleic Acids Res 2007; 36:D907-12. [PMID: 17986454 PMCID: PMC2238933 DOI: 10.1093/nar/gkm948] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.
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Affiliation(s)
- Yasushi Okuno
- Department of PharmacoInformatics, Center for Integrative Education of Pharmacy Frontier, Graduate School of Pharmaceutical Sciences, Kyoto University, Japan.
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Nordström A. Data Mining for Metabolomics. METABOLOMICS, METABONOMICS AND METABOLITE PROFILING 2007. [DOI: 10.1039/9781847558107-00273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Anders Nordström
- The Scripps Research Institute Scripps Center For Mass Spectrometry BCC007 10550 North Torrey Pines Road La Jolla CA 92122 USA
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Seiler KP, George GA, Happ MP, Bodycombe NE, Carrinski HA, Norton S, Brudz S, Sullivan JP, Muhlich J, Serrano M, Ferraiolo P, Tolliday NJ, Schreiber SL, Clemons PA. ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res 2007; 36:D351-9. [PMID: 17947324 PMCID: PMC2238881 DOI: 10.1093/nar/gkm843] [Citation(s) in RCA: 203] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.
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Affiliation(s)
- Kathleen Petri Seiler
- Chemical Biology Program and Platform, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA
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Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O, Goryanin I. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 2007; 3:135. [PMID: 17882155 PMCID: PMC2013923 DOI: 10.1038/msb4100177] [Citation(s) in RCA: 334] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2007] [Accepted: 07/30/2007] [Indexed: 02/06/2023] Open
Abstract
A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.
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Affiliation(s)
- Hongwu Ma
- Computational Systems Biology, School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Anatoly Sorokin
- Computational Systems Biology, School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Alexander Mazein
- Computational Systems Biology, School of Informatics, The University of Edinburgh, Edinburgh, UK
| | | | | | - Oleg Demin
- Institute for Systems Biology SPb, Leninskie Gory, Moscow State University, Moscow, Russia
| | - Igor Goryanin
- Computational Systems Biology, School of Informatics, The University of Edinburgh, Edinburgh, UK
- Computational Systems Biology, School of Informatics, The University of Edinburgh, Darwin Building, Kings Buildings, Mayfield Road, Edinburgh EH9 3JB, UK. Tel.: +44 131 651 3837; Fax: +44 131 650 6513;
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Lisacek F, Hoogland C, Lescuyer P, Hochstrasser DF, Appel RD. Using bioinformatic resources in the proteomic analysis of biological fluids. Proteomics Clin Appl 2007; 1:900-15. [PMID: 21136743 DOI: 10.1002/prca.200700188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2007] [Indexed: 12/24/2022]
Abstract
On-line databases targeted towards protein contents in biological fluids are scarce. Consequently, the investigation of proteins identified in a biological fluid most importantly depends on crosschecking information gathered from less specific resources. This review summarises the key databases and tools for collecting information on tissue specificity or expression profiles. It also emphasises the high connectivity between databases fruitfully used to corroborate and piece information together. Finally, selected issues related to appropriate bioinformatics tools in the context of clinical applications are succinctly discussed.
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Affiliation(s)
- Frédérique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland.
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Brusic V, Marina O, Wu CJ, Reinherz EL. Proteome informatics for cancer research: from molecules to clinic. Proteomics 2007; 7:976-91. [PMID: 17370257 DOI: 10.1002/pmic.200600965] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.
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Affiliation(s)
- Vladimir Brusic
- Cancer Vaccine Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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Abstract
We present a new version of the European Bioinformatics Institute Web Services, a complete suite of SOAP-based web tools for structural and functional analysis, with new and improved applications. New functionality has been added to most of the services already available, and an improved version of the underlying framework has allowed us to include more applications. Information on the EBI Web Services, tutorials and clients can be found at http://www.ebi.ac.uk/Tools/webservices.
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Affiliation(s)
| | | | | | - Rodrigo Lopez
- *To whom correspondence should be addressed. +44 1223 494423+44 1223 494468
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Wishart DS. In Silico Drug Exploration and Discovery Using DrugBank. ACTA ACUST UNITED AC 2007; Chapter 14:Unit 14.4. [DOI: 10.1002/0471250953.bi1404s18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- David S. Wishart
- University of Alberta and the National Institute of Nanotechnology (NINT) National Research Council Edmonton Alberta Canada
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