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Warr WA. Many InChIs and quite some feat. J Comput Aided Mol Des 2015; 29:681-94. [PMID: 26081259 DOI: 10.1007/s10822-015-9854-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 06/10/2015] [Indexed: 12/14/2022]
Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, Holmes Chapel, Crewe, Cheshire, CW4 7HZ, UK,
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2
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Abstract
Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.
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3
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A set of descriptors for identifying the protein-drug interaction in cellular networking. J Theor Biol 2014; 359:120-8. [PMID: 24949993 DOI: 10.1016/j.jtbi.2014.06.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/02/2014] [Accepted: 06/06/2014] [Indexed: 12/24/2022]
Abstract
The study of protein-drug interactions is a significant issue for drug development. Unfortunately, it is both expensive and time-consuming to perform physical experiments to determine whether a drug and a protein are interacting with each other. Some previous attempts to design an automated system to perform this task were based on the knowledge of the 3D structure of a protein, which is not always available in practice. With the availability of protein sequences generated in the post-genomic age, however, a sequence-based solution to deal with this problem is necessary. Following other works in this area, we propose a new machine learning system based on several protein descriptors extracted from several protein representations, such as, variants of the position specific scoring matrix (PSSM) of proteins, the amino-acid sequence, and a matrix representation of a protein. The prediction engine is operated by an ensemble of support vector machines (SVMs), with each SVM trained on a specific descriptor and the results of each SVM combined by sum rule. The overall success rate achieved by our final ensemble is notably higher than previous results obtained on the same datasets using the same testing protocols reported in the literature. MATLAB code and the datasets used in our experiments are freely available for future comparison at http://www.dei.unipd.it/node/2357.
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Southan C, Sitzmann M, Muresan S. Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database. Mol Inform 2013; 32:881-897. [PMID: 24533037 PMCID: PMC3916886 DOI: 10.1002/minf.201300103] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Accepted: 11/13/2013] [Indexed: 02/01/2023]
Abstract
ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture.
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Affiliation(s)
- Christopher Southan
- IUPHAR Database and Guide to PHARMACOLOGY web portal Group, The University/British Heart Foundation Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh EdinburghEH164TJ, UK
| | - Markus Sitzmann
- Chemical Biology Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of HealthFrederick, 21702 MD, USA
| | - Sorel Muresan
- Food Control Department, Banat’s University of Agricultural Sciences and Veterinary MedicineCalea Aradului 119, 300645 Timisoara, Romania
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5
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Min JL, Xiao X, Chou KC. iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking. BIOMED RESEARCH INTERNATIONAL 2013; 2013:701317. [PMID: 24371828 PMCID: PMC3858977 DOI: 10.1155/2013/701317] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/17/2013] [Indexed: 01/16/2023]
Abstract
With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because three-dimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called "iEzy-Drug," in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou's pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results.
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Affiliation(s)
- Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
- Information School, ZheJiang Textile & Fashion College, NingBo 315211, China
- Gordon Life Science Institute, Belmont, MA 02478, USA
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Belmont, MA 02478, USA
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
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6
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Xiao X, Min JL, Wang P, Chou KC. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 2013; 8:e72234. [PMID: 24015221 PMCID: PMC3754978 DOI: 10.1371/journal.pone.0072234] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 07/08/2013] [Indexed: 11/19/2022] Open
Abstract
Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
- Information School, ZheJiang Textile and Fashion College, NingBo, China
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Pu Wang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
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7
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Xiao X, Min JL, Wang P, Chou KC. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J Theor Biol 2013; 337:71-9. [PMID: 23988798 DOI: 10.1016/j.jtbi.2013.08.013] [Citation(s) in RCA: 104] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 07/26/2013] [Accepted: 08/14/2013] [Indexed: 12/29/2022]
Abstract
Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China; Information School, Zhe-Jiang Textile & Fashion College, Ning-Bo 315211, China; Gordon Life Science Institute, 53 South Cottage Road, Belmont, MA 02478, United States.
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.
| | - Pu Wang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia; Gordon Life Science Institute, 53 South Cottage Road, Belmont, MA 02478, United States.
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8
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Abstract
The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein-ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided.
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Affiliation(s)
- Xavier Jalencas
- Chemogenomics Laboratory, Research Programme on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute and University Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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9
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Montolio M, Gregori-Puigjané E, Pineda D, Mestres J, Navarro P. Identification of small molecule inhibitors of amyloid β-induced neuronal apoptosis acting through the imidazoline I(2) receptor. J Med Chem 2012; 55:9838-46. [PMID: 23098038 DOI: 10.1021/jm301055g] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Aberrant activation of signaling pathways plays a pivotal role in central nervous system disorders, such as Alzheimer's disease (AD). Using a combination of virtual screening and experimental testing, novel small molecule inhibitors of tPA-mediated extracellular signal-regulated kinase (Erk)1/2 activation were identified that provide higher levels of neuroprotection from Aβ-induced apoptosis than Memantine, the most recently FDA-approved drug for AD treatment. Subsequent target deconvolution efforts revealed that they all share low micromolar affinity for the imidazoline I(2) receptor, while being devoid of any significant affinity to a list of AD-relevant targets, including the N-methyl-d-aspartate receptor (NMDAR), acetylcholinesterase (AChE), and monoamine oxidase B (MAO-B). Targeting the imidazoline I(2) receptor emerges as a new mechanism of action to inhibit tPA-induced signaling in neurons for the treatment of AD and other neurodegenerative diseases.
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Affiliation(s)
- Marisol Montolio
- Cancer Research Program, IMIM-Hospital del Mar Research Institute and University Pompeu Fabra, Parc de Recerca Biomèdica (PRBB), Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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10
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Carrascosa MC, Massaguer OL, Mestres J. PharmaTrek: A Semantic Web Explorer for Open Innovation in Multitarget Drug Discovery. Mol Inform 2012; 31:537-541. [PMID: 23548981 PMCID: PMC3573647 DOI: 10.1002/minf.201200070] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Accepted: 07/13/2012] [Indexed: 11/10/2022]
Affiliation(s)
- Maria C Carrascosa
- Research Programme on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute and University Pompeu Fabra , Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
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