Showing papers by "David S. Wishart published in 2008"
TL;DR: The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release and contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs.
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.
2,380 citations
TL;DR: This review focuses on the recent trends and potential applications of metabolomics in four areas of food science and technology: (1) food component analysis; (2) food quality/authenticity assessment; (3) food consumption monitoring; and (4) physiological monitoring in food intervention or diet challenge studies.
Abstract: Metabolomics is an emerging field of “omics” research that focuses on high-throughput characterization of small molecule metabolites in biological matrices. As such, metabolomics is ideally positioned to be used in many areas of food science and nutrition research. This review focuses on the recent trends and potential applications of metabolomics in four areas of food science and technology: (1) food component analysis; (2) food quality/authenticity assessment; (3) food consumption monitoring; and (4) physiological monitoring in food intervention or diet challenge studies.
604 citations
TL;DR: Some of the practical aspects pertaining to NMR-based quantitative metabolomics is described and some of the strengths, limitations and applications of this particular approach to metabolomics are highlighted.
Abstract: Nuclear magnetic resonance (NMR) spectroscopy can be used to both identify and quantify chemicals from complex mixtures. This can be done semi-automatically by comparing the mixture of interest to a library of reference spectra derived from pure compounds of known concentrations. This particular approach is now being exploited to characterize the metabolomes of many different biological samples in what is called quantitative metabolomics or targeted metabolic profiling. This review describes some of the practical aspects pertaining to NMR-based quantitative metabolomics and highlights some of the strengths, limitations and applications of this particular approach to metabolomics.
531 citations
TL;DR: This work has chosen to characterize CSF as the first biofluid to be intensively scrutinized and identified and quantify essentially all of the metabolites that can be commonly detected in the human CSF metabolome.
317 citations
TL;DR: The PolySearch web server, a web-based tool that supports comprehensive queries in genomics, proteomics or metabolomics, and exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences.
Abstract: A particular challenge in biomedical text mining is to find ways of handling ‘comprehensive’ or ‘associative’ queries such as ‘Find all genes associated with breast cancer’. Given that many queries in genomics, proteomics or metabolomics involve these kind of comprehensive searches we believe that a web-based tool that could support these searches would be quite useful. In response to this need, we have developed the PolySearch web server. PolySearch supports >50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is ‘Given X, find all Y's’ where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. PolySearch's performance has been assessed in tasks such as gene synonym identification, protein–protein interaction identification and disease gene identification using a variety of manually assembled ‘gold standard’ text corpuses. Its f-measure on these tasks is 88, 81 and 79%, respectively. These values are between 5 and 50% better than other published tools. The server is freely available at http://wishart.biology.ualberta.ca/polysearch
248 citations
TL;DR: Tests conducted on more than 100 proteins from the BioMagResBank indicate that CS23D converges for >95% of protein queries, and these chemical shift generated structures were found to be within 0.2–2.8 Å RMSD of the NMR structure generated using conventional NOE-base NMR methods or conventional X-ray methods.
Abstract: CS23D (chemical shift to 3D structure) is a web server for rapidly generating accurate 3D protein structures using only assigned nuclear magnetic resonance (NMR) chemical shifts and sequence data as input. Unlike conventional NMR methods, CS23D requires no NOE and/or J-coupling data to perform its calculations. CS23D accepts chemical shift files in either SHIFTY or BMRB formats, and produces a set of PDB coordinates for the protein in about 10-15 min. CS23D uses a pipeline of several preexisting programs or servers to calculate the actual protein structure. Depending on the sequence similarity (or lack thereof) CS23D uses either (i) maximal subfragment assembly (a form of homology modeling), (ii) chemical shift threading or (iii) shift-aided de novo structure prediction (via Rosetta) followed by chemical shift refinement to generate and/or refine protein coordinates. Tests conducted on more than 100 proteins from the BioMagResBank indicate that CS23D converges (i.e. finds a solution) for >95% of protein queries. These chemical shift generated structures were found to be within 0.2-2.8 A RMSD of the NMR structure generated using conventional NOE-base NMR methods or conventional X-ray methods. The performance of CS23D is dependent on the completeness of the chemical shift assignments and the similarity of the query protein to known 3D folds. CS23D is accessible at http://www.cs23d.ca.
208 citations
TL;DR: This review explores some of the most interesting or significant applications of metabolomics as they relate to pharmaceutical research and development, showing that metabolomics potentially offer drug researchers and drug regulators an effective, inexpensive route to addressing many of the riskier or more expensive issues associated with the discovery, development and monitoring of drug products.
Abstract: Metabolomics is a relatively new field of 'omics' technology that is primarily concerned with the global or system-wide characterization of small molecule metabolites using technologies such as nuclear magnetic resonance, liquid chromatography and/or mass spectrometry. Its unique focus on small molecules and the physiological effects of small molecules aligns the field of metabolomics very closely with the aims and interests of many researchers in the pharmaceutical industry. Because of its conceptual and technical overlap with many aspects of pharmaceutical research, metabolomics is now finding applications that span almost the full length of the drug discovery and development pipeline, from lead compound discovery to post-approval drug surveillance. This review explores some of the most interesting or significant applications of metabolomics as they relate to pharmaceutical research and development. Specific examples are given that show how metabolomics can be used to facilitate lead compound discovery, to improve biomarker identification (for monitoring disease status and drug efficacy) and to monitor drug metabolism and toxicity. Other applications are also discussed, including the use of metabolomics to facilitate clinical trial testing and to improve post-approval drug monitoring. These examples show that metabolomics potentially offer drug researchers and drug regulators an effective, inexpensive route to addressing many of the riskier or more expensive issues associated with the discovery, development and monitoring of drug products.
200 citations
TL;DR: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra.
Abstract: Background
One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies
173 citations
TL;DR: A more detailed investigation of the Random Coil Index method is undertaken, finding that the RCI method is very robust and that it represents a useful addition to traditional methods of studying protein flexibility.
Abstract: Protein flexibility lies at the heart of many protein–ligand binding events and enzymatic activities. However, the experimental measurement of protein motions is often difficult, tedious and error-prone. As a result, there is a considerable interest in developing simpler and faster ways of quantifying protein flexibility. Recently, we described a method, called Random Coil Index (RCI), which appears to be able to quantitatively estimate model-free order parameters and flexibility in protein structural ensembles using only backbone chemical shifts. Because of its potential utility, we have undertaken a more detailed investigation of the RCI method in an attempt to ascertain its underlying principles, its general utility, its sensitivity to chemical shift errors, its sensitivity to data completeness, its applicability to other proteins, and its general strengths and weaknesses. Overall, we find that the RCI method is very robust and that it represents a useful addition to traditional methods of studying protein flexibility. We have implemented many of the findings and refinements reported here into a web server that allows facile, automated predictions of model-free order parameters, MD RMSF and NMR RMSD values directly from backbone 1H, 13C and 15N chemical shift assignments. The server is available at http://wishart.biology.ualberta.ca/rci
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109 citations
TL;DR: PROTEUS2 is a web server designed to support comprehensive protein structure prediction and structure-based annotation and provides high quality 3D models that compare favorably with those generated by SWISS-MODEL and 3D JigSaw.
Abstract: PROTEUS2 is a web server designed to support comprehensive protein structure prediction and structure-based annotation. PROTEUS2 accepts either single sequences (for directed studies) or multiple sequences (for whole proteome annotation) and predicts the secondary and, if possible, tertiary structure of the query protein(s). Unlike most other tools or servers, PROTEUS2 bundles signal peptide identification, transmembrane helix prediction, transmembrane beta-strand prediction, secondary structure prediction (for soluble proteins) and homology modeling (i.e. 3D structure generation) into a single prediction pipeline. Using a combination of progressive multi-sequence alignment, structure-based mapping, hidden Markov models, multi-component neural nets and up-to-date databases of known secondary structure assignments, PROTEUS is able to achieve among the highest reported levels of predictive accuracy for signal peptides (Q2 = 94%), membrane spanning helices (Q2 = 87%) and secondary structure (Q3 score of 81.3%). PROTEUS2's homology modeling services also provide high quality 3D models that compare favorably with those generated by SWISS-MODEL and 3D JigSaw (within 0.2 A RMSD). The average PROTEUS2 prediction takes approximately 3 min per query sequence. The PROTEUS2 server along with source code for many of its modules is accessible a http://wishart.biology.ualberta.ca/proteus2.
78 citations
TL;DR: An overview of the DrugBank database, which combines detailed drug data with comprehensive drug-target and drug-action information, and how it can facilitate pharmacogenomic research is provided.
Abstract: DrugBank is a freely available web-enabled database that combines detailed drug data with comprehensive drug-target and drug-action information. It was specifically designed to facilitate in silico drug-target discovery, drug design, drug-metabolism prediction, drug-interaction prediction, and general pharmaceutical education. One of the most unique and useful components of the DrugBank database is the information it contains on drug metabolism, drug-metabolizing enzymes and drug-target polymorphisms. As pharmacogenomics is fundamentally concerned with the role of genes and genetic variation of how an individual responds to a drug, DrugBank is able to offer a convenient venue to explore pharmacogenomic questions in silico. This paper provides a brief overview on DrugBank and how it can facilitate pharmacogenomic research.
TL;DR: The results suggest that the interior hydration plays an important role in the structural stability of fibrils.
TL;DR: The application of metabolomics to kidney transplant monitoring is still in its early stages, but there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of kidney function, kidney injury, and immunosuppressive drug toxicity.
Abstract: Renal transplant success is closely tied to the ability to monitor transplant recipients and responsively change their medications. However, transplant monitoring still depends on relatively dated technologies - serum creatinine levels, urine output, and histopathology of biopsy samples. These techniques do not offer sufficient specificity, sensitivity, or accuracy for appropriate and timely interventions. As a result, more specific diagnostic techniques, based on proteomics, genomics and metabolomics are being sought. Metabolomics (the high-throughput measurement and analysis of metabolites) may make it possible to monitor transplants more effectively and specifically. Changes in the concentration profiles of a number of small molecule metabolites found in either blood or urine can be used to localize kidney damage, assess organs at risk of rejection, assess kidneys suffering from ischemiareperfusion injury or identify organs that have been damaged by immunosuppressive drugs. The application of metabolomics to kidney transplant monitoring is still in its early stages. Nevertheless, there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of kidney function, kidney injury, and immunosuppressive drug toxicity. Metabolomics could serve as a good complement to existing proteomic and genomic technologies.
TL;DR: KS10 is a novel phage with a genomic organization that differs from most phages in that its capsid genes are not aligned into one module but rather separated by approximately 11 kb, giving evidence of one or more prior genetic rearrangements.
Abstract: The Burkholderia cepacia complex (BCC) is a versatile group of Gram negative organisms that can be found throughout the environment in sources such as soil, water, and plants. While BCC bacteria can be involved in beneficial interactions with plants, they are also considered opportunistic pathogens, specifically in patients with cystic fibrosis and chronic granulomatous disease. These organisms also exhibit resistance to many antibiotics, making conventional treatment often unsuccessful. KS10 was isolated as a prophage of B. cenocepacia K56-2, a clinically relevant strain of the BCC. Our objective was to sequence the genome of this phage and also determine if this prophage encoded any virulence determinants. KS10 is a 37,635 base pairs (bp) transposable phage of the opportunistic pathogen Burkholderia cenocepacia. Genome sequence analysis and annotation of this phage reveals that KS10 shows the closest sequence homology to Mu and BcepMu. KS10 was found to be a prophage in three different strains of B. cenocepacia, including strains K56-2, J2315, and C5424, and seven tested clinical isolates of B. cenocepacia, but no other BCC species. A survey of 23 strains and 20 clinical isolates of the BCC revealed that KS10 is able to form plaques on lawns of B. ambifaria LMG 19467, B. cenocepacia PC184, and B. stabilis LMG 18870. KS10 is a novel phage with a genomic organization that differs from most phages in that its capsid genes are not aligned into one module but rather separated by approximately 11 kb, giving evidence of one or more prior genetic rearrangements. There were no potential virulence factors identified in KS10, though many hypothetical proteins were identified with no known function.
TL;DR: Protein contact order can be effectively predicted from the primary sequence, at the absence of three-dimensional structure, using several simple yet very effective methods.
Abstract: Contact order is a topological descriptor that has been shown to be correlated with several interesting protein properties such as protein folding rates and protein transition state placements. Contact order has also been used to select for viable protein folds from ab initio protein structure prediction programs. For proteins of known three-dimensional structure, their contact order can be calculated directly. However, for proteins with unknown three-dimensional structure, there is no effective prediction method currently available. In this paper, we propose several simple yet very effective methods to predict contact order from the amino acid sequence only. One set of methods is based on a weighted linear combination of predicted secondary structure content and amino acid composition. Depending on the number of components used in these equations it is possible to achieve a correlation coefficient of 0.857–0.870 between the observed and predicted contact order. A second method, based on sequence similarity to known three-dimensional structures, is able to achieve a correlation coefficient of 0.977. We have also developed a much more robust implementation for calculating contact order directly from PDB coordinates that works for > 99% PDB files. All of these contact order predictors and calculators have been implemented as a web server (see Availability and requirements section for URL). Protein contact order can be effectively predicted from the primary sequence, at the absence of three-dimensional structure. Three factors, percentage of residues in alpha helices, percentage of residues in beta strands, and sequence length, appear to be strongly correlated with the absolute contact order.
TL;DR: It is demonstrated that binding activity of scFV/Qdot conjugates can be improved through structure-based genetic engineering of the scFv.
TL;DR: Previous evidence indicating lipid rafts of monocytic cells are specialized for cytoskeletal assembly and vesicle trafficking is confirmed, and the combination of LC-ESI and LC-MALDI MS/MS increases the proteome coverage which allows better understanding of the lipid raft function.
01 Jan 2008
TL;DR: This chapter illustrates how putative drug targets and drug leads for exogenous diseases can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input.
Abstract: The availability of three-dimensional (3D) models of both drug leads (small molecule ligands) and drug targets (proteins) is essential to molecular docking and computational drug discovery. This chapter describes an emerging methodology that can be used to identify both drug leads and drug targets using three newly developed web-accessible databases: 1) DrugBank; 2) The Human Metabolome Database; and 3) PubChem. Specifically, it illustrates how putative drug targets and drug leads for exogenous diseases (i.e., infectious diseases) can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input. It also illustrates how putative drug targets and drug leads for endogenous diseases (i.e., non-infectious diseases or chronic conditions) can be identified using similar databases and similar sequence input. This chapter is intended to illustrate how bioinformatics and cheminformatics can work synergistically to help provide the necessary inputs for computer-aided drug design.