David S. Wishart

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Canadian Researcher David S. Wishart.jpg
Canadian Researcher

|thumb|David S. Wishart]]

David Wishart
FRSC
BornDecember 7, 1961 (1961-12-07) (age 62)
Edmonton, Alberta, Canada
Alma mater University of Alberta
Yale University
OccupationProfessor
Known for
Awards
  • Canadian Research Chair Tier 1 (2023)
  • J. Gordin Kaplan Award for Excellence in Research (2023)
  • Distinguished University Professor (2018)
  • University of Alberta Alumni Award (2017)
  • ASTech Awards (1999, 2021)
  • Lifetime Fellowship, Metabolomics Society (2014)
Scientific career
Fields
InstitutionsUniversity of Alberta
Website www.wishartlab.com , www.tmicwishartnode.ca

David S. Wishart FRSC (born December 7, 1961) is a Canadian researcher in metabolomics and a Distinguished University Professor in the Department of Biological Sciences and the Department of Computing Science at the University of Alberta. Wishart also holds cross appointments in the Faculty of Pharmacy and Pharmaceutical Sciences and the Department of Laboratory Medicine and Pathology in the Faculty of Medicine and Dentistry. Additionally, Wishart holds a joint appointment in metabolomics at the Pacific Northwest National Laboratory in Richland, Washington. Wishart is well known for his pioneering contributions to the fields of protein NMR spectroscopy, bioinformatics, cheminformatics and metabolomics. [1] [2] [3] In 2011, Wishart founded the Metabolomics Innovation Centre (TMIC), [4] which is Canada's national metabolomics laboratory.

Contents

Wishart is also a biotech entrepreneur. Since 1995 he has launched eight start-up biotech companies, including Chenomx, [5] OMx Personal Health Analytics [6] and Molecular You Corp. [7]  With more than 500 publications and >100,000 citations over his career, [8] he has been consistently ranked among the world's most cited scientists in any discipline [9] [10] and among the world's most cited 200 life scientists. [11]

Early life and education

Wishart was born and raised in Edmonton, Alberta, Canada and has one brother, Ian (a physician) and one sister, Sandy. His mother, Patricia worked as a naturalist and author; his father William was a wildlife biologist with the government of Alberta. Wishart identifies as Metis. He has both Cree and Assiniboine ancestry from his father's side and Scottish ancestry from his mother's side. As a youth, Wishart learned to hunt, fish and trap from his father, who also used to operate his own trapline.

Wishart received his B.Sc. (Honours, First Class) in physics from the University of Alberta in 1983 and his M.Phil. (1986) and Ph.D. degrees (1991) in molecular biophysics from Yale University. Wishart completed his Ph.D. under the supervision of Frederic M. Richards and his post-doctoral studies (1991–1995) under the supervision of Brian D. Sykes. [12]

Academic career

Wishart started his academic career as an assistant professor in 1995 with the Faculty of Pharmacy and Pharmaceutical Sciences at the University of Alberta where he held the Bristol Myers Squibb Chair in Biotechnology for 10 years. He was promoted to associate professor in 2002 and full professor in 2003, joining the Departments of Computing Science and Biological Sciences in the Faculty of Science at the University of Alberta. Because of his growing involvement in clinical chemistry, Wishart was appointed as an adjunct professor in the Department of Laboratory Medicine and Pathology in 2012. Wishart was appointed as a Distinguished University Professor in 2018. [13] From 2004-2016, Wishart also served as a senior research officer and the director of nanobiology at the National Research Council of Canada with the National Institute of Nanotechnology, located on the University of Alberta campus.

Research

Wishart's research interests span a number of areas including structural biology, computational biology, bioinformatics, nanobiology, metabolomics, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. A common theme to his research career has been the development of techniques, technology cores, protocols, data resources or computer programs that make science simpler, faster, cheaper or easier. [1] [2] [3]

Biomolecular NMR

Wishart began his research career in the field of protein NMR in the early 1990s, focusing on using NMR spectroscopy to characterize protein structure and protein denaturation. At the time, protein structural analysis by NMR required hundreds of hours of manual data analysis and data tabulation. In an effort to accelerate the process, Wishart discovered a trend with regard to how the NMR chemical shifts of amino acid residues changed systematically with regard to their secondary structure. He proceeded to develop a technique, called the chemical shift index, also known as CSI, that used a set of simple rules and simple chemical shift tables that allowed scientists to directly use protein chemical shift assignments to rapidly determine the type and location of protein secondary structures in proteins in just seconds. [14] [15] Subsequently, Wishart showed how NMR chemical shifts could be used to easily and rapidly measure protein flexibility via the random coil index or RCI. [16] [17] Later, he showed how it was possible to use chemical shifts to determine protein backbone torsion angles with a program called PREDITOR. [18] Wishart also determined how chemical shifts could be used to measure residue accessible surface area, [19] and to identify super secondary structure elements. [19] [20] To further extend this work, Wishart developed innovative methods to determine the 3D structure of proteins using a technique called chemical shift threading with programs such as GeNMR, CS23D and E-Thrifty. [21] [22] [23] To help compare and assess existing protein NMR structures, Wishart also developed methods to accurately predict protein chemical shifts from 3D coordinates using programs such as ShiftX and ShiftX2. [24] [25] At the same time he also developed methods to re-reference incorrectly assigned protein chemical shifts using programs such as SHIFTCOR and PANAV. [26] These programs were used to help create protein NMR databases such as RefDB that contain 1000s of re-referenced chemical shifts. [27] Wishart's papers describing these NMR methods have been cited more than 15,000 times and are now considered[ by whom? ] to be foundational techniques for much of modern protein NMR.

Metabolomics

In the early 2000s, Wishart turned his attention from looking at big molecules such as proteins to looking at small molecules (metabolites). In 2001 he developed and then patented NMR-based techniques (leading to the spin-off company Chenomx [5] ) that permitted the rapid identification and quantification of metabolites by NMR in biofluids. [28] In 2005, he conceived of the Human Metabolome Project (HMP) [29] – the metabolomic equivalent of the Human Genome Project. Wishart raised over $10 million in funding from Genome Canada and launched a multi-institutional, pan-Canadian program to systematically identify all metabolites, drugs and xenobiotics in clinically important human biofluids. The goal of HMP is to provide the scientific community with easily accessible reference data about human metabolites, thereby making metabolomic data analysis more comprehensive and much simpler. As of 2022, the HMP is still ongoing and has led to the identification of more than 240,000 human metabolites, 6000 drugs and drug metabolites, 70,000 food constituents and 3000 toxins and contaminants. [30] [31] [32] This information, along with many tools to facilitate metabolite identification and interpretation, have been archived in several publicly available databases created by the Wishart lab. These include the Human Metabolome Database (HMDB), which contains data on human metabolites and their structures, along with descriptions, NMR spectra and MS spectra. [33] Another resource developed from the project is DrugBank, [31] a database of all known, approved drugs and their target molecules. Other databases developed by the Wishart lab include FooDB, [32] a database of food constituents and food additives; and T3DB, [34] a database of toxic compounds and contaminants as well as their toxicological effects.

In 2011, Wishart founded The Metabolomics Innovation Centre (TMIC) and served as its first director (2011-2019). [35] Wishart's laboratory within TMIC houses over $8 million in modern LC-MS, GC-MS and NMR equipment. His lab routinely processes more than 20,000 samples each year. Using this wide array of equipment, Wishart helped develop a number of quantitative metabolomics techniques for NMR [36] [37] and liquid chromatography mass spectrometry. [38] [39]   Using these methods, Wishart and his team have conducted comprehensive, quantitative metabolome analyses of human serum, [40] urine, [41] saliva, [42] cerebrospinal fluid [43] and feces. [44]

Computational biology and open science

Wishart has made all his lab's data resources, computer programs, algorithms and techniques publicly accessible. This open science/open access initiative has been aimed at providing tools and techniques to make biomolecular NMR, metabolomics, structural biology and a number of related techniques more accessible for all scientists. So far, this initiative has led Wishart's lab to develop and release more than 100 publicly accessible web servers and web-based databases, [45] including NP-MRD [46] and CFM-ID. [47] To further his open-science efforts, Wishart co-founded several educational bioinformatics programs such as the Canadian Bioinformatics Workshops and has been actively involved in other international standardization and open-source initiatives to make computational biology resources more widely available and accessible.

Personal life

Wishart is married to Debby Waldman, a freelance writer and editor from Utica, New York. He has two children: Elizabeth, an epidemiologist; and Noah, a civil engineer. They all live and work in Edmonton, Alberta.[ citation needed ]

Related Research Articles

<span class="mw-page-title-main">Metabolomics</span> Scientific study of chemical processes involving metabolites

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.

<span class="mw-page-title-main">Metabolome</span> Complete set of small molecules in a biological sample

The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.

The DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets created and maintained by the University of Alberta and The Metabolomics Innovation Centre located in Alberta, Canada. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug data with comprehensive drug target information. DrugBank has used content from Wikipedia; Wikipedia also often links to Drugbank, posing potential circular reporting issues.

<span class="mw-page-title-main">Human Metabolome Database</span> Database of human metabolites

The Human Metabolome Database (HMDB) is a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body. It has been created by the Human Metabolome Project funded by Genome Canada and is one of the first dedicated metabolomics databases. The HMDB facilitates human metabolomics research, including the identification and characterization of human metabolites using NMR spectroscopy, GC-MS spectrometry and LC/MS spectrometry. To aid in this discovery process, the HMDB contains three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data (Fig. 1–3). The chemical data includes 41,514 metabolite structures with detailed descriptions along with nearly 10,000 NMR, GC-MS and LC/MS spectra.

<span class="mw-page-title-main">Toxin and Toxin-Target Database</span>

The Toxin and Toxin-Target Database (T3DB), also known as the Toxic Exposome Database, is a freely accessible online database of common substances that are toxic to humans, along with their protein, DNA or organ targets. The database currently houses nearly 3,700 toxic compounds or poisons described by nearly 42,000 synonyms. This list includes various groups of toxins, including common pollutants, pesticides, drugs, food toxins, household and industrial/workplace toxins, cigarette toxins, and uremic toxins. These toxic substances are linked to 2,086 corresponding protein/DNA target records. In total there are 42,433 toxic substance-toxin target associations. Each toxic compound record (ToxCard) in T3DB contains nearly 100 data fields and holds information such as chemical properties and descriptors, mechanisms of action, toxicity or lethal dose values, molecular and cellular interactions, medical information, NMR an MS spectra, and up- and down-regulated genes. This information has been extracted from over 18,000 sources, which include other databases, government documents, books, and scientific literature.

The Small Molecule Pathway Database (SMPDB) is a comprehensive, high-quality, freely accessible, online database containing more than 600 small molecule (i.e. metabolic) pathways found in humans. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing colorful, detailed, fully searchable, hyperlinked diagrams of five types of small molecule pathways: 1) general human metabolic pathways; 2) human metabolic disease pathways; 3) human metabolite signaling pathways; 4) drug-action pathways and 5) drug metabolism pathways. SMPDB pathways may be navigated, viewed and zoomed interactively using a Google Maps-like interface. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures (Fig. 1). Each small molecule in SMPDB is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. Additionally, all SMPDB pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram. Users can browse the SMPDB (Fig. 2) or search its contents by text searching (Fig. 3), sequence searching, or chemical structure searching. More powerful queries are also possible including searching with lists of gene or protein names, drug names, metabolite names, GenBank IDs, Swiss-Prot IDs, Agilent or Affymetrix microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB's mapping interface.

MetaboAnalyst is a set of online tools for metabolomic data analysis and interpretation, created by members of the Wishart Research Group at the University of Alberta. It was first released in May 2009 and version 2.0 was released in January 2012. MetaboAnalyst provides a variety of analysis methods that have been tailored for metabolomic data. These methods include metabolomic data processing, normalization, multivariate statistical analysis, and data annotation. The current version is focused on biomarker discovery and classification.

<span class="mw-page-title-main">Random coil index</span> Protocol in biochemistry

Random coil index (RCI) predicts protein flexibility by calculating an inverse weighted average of backbone secondary chemical shifts and predicting values of model-free order parameters as well as per-residue RMSD of NMR and molecular dynamics ensembles from this parameter.

<span class="mw-page-title-main">Christoph Steinbeck</span> German chemist (born 1966)

Christoph Steinbeck is a German chemist and has a professorship for analytical chemistry, cheminformatics and chemometrics at the Friedrich-Schiller-Universität Jena in Thuringia.

<span class="mw-page-title-main">CS23D</span>

CS23D is a web server to generate 3D structural models from NMR chemical shifts. CS23D combines maximal fragment assembly with chemical shift threading, de novo structure generation, chemical shift-based torsion angle prediction, and chemical shift refinement. CS23D makes use of RefDB and ShiftX.

<span class="mw-page-title-main">Chemical shift index</span> Laboratory technique

The chemical shift index or CSI is a widely employed technique in protein nuclear magnetic resonance spectroscopy that can be used to display and identify the location as well as the type of protein secondary structure found in proteins using only backbone chemical shift data The technique was invented by David S. Wishart in 1992 for analyzing 1Hα chemical shifts and then later extended by him in 1994 to incorporate 13C backbone shifts. The original CSI method makes use of the fact that 1Hα chemical shifts of amino acid residues in helices tends to be shifted upfield relative to their random coil values and downfield in beta strands. Similar kinds of upfield and downfield trends are also detectable in backbone 13C chemical shifts.

The Yeast Metabolome Database (YMDB) is a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in or produced by Saccharomyces cerevisiae. The YMDB was designed to facilitate yeast metabolomics research, specifically in the areas of general fermentation as well as wine, beer and fermented food analysis. YMDB supports the identification and characterization of yeast metabolites using NMR spectroscopy, GC-MS spectrometry and Liquid chromatography–mass spectrometry. The YMDB contains two kinds of data: 1) chemical data and 2) molecular biology/biochemistry data. The chemical data includes 2027 metabolite structures with detailed metabolite descriptions along with nearly 4000 NMR, GC-MS and LC/MS spectra.

Protein chemical shift prediction is a branch of biomolecular nuclear magnetic resonance spectroscopy that aims to accurately calculate protein chemical shifts from protein coordinates. Protein chemical shift prediction was first attempted in the late 1960s using semi-empirical methods applied to protein structures solved by X-ray crystallography. Since that time protein chemical shift prediction has evolved to employ much more sophisticated approaches including quantum mechanics, machine learning and empirically derived chemical shift hypersurfaces. The most recently developed methods exhibit remarkable precision and accuracy.

Metabolite Set Enrichment Analysis (MSEA) is a method designed to help metabolomics researchers identify and interpret patterns of metabolite concentration changes in a biologically meaningful way. It is conceptually similar to another widely used tool developed for transcriptomics called Gene Set Enrichment Analysis or GSEA. GSEA uses a collection of predefined gene sets to rank the lists of genes obtained from gene chip studies. By using this “prior knowledge” about gene sets researchers are able to readily identify significant and coordinated changes in gene expression data while at the same time gaining some biological context. MSEA does the same thing by using a collection of predefined metabolite pathways and disease states obtained from the Human Metabolome Database. MSEA is offered as a service both through a stand-alone web server and as part of a larger metabolomics analysis suite called MetaboAnalyst.

FooDB is a freely available, open-access database containing chemical composition data on common, unprocessed foods. It also contains extensive data on flavour and aroma constituents, food additives as well as positive and negative health effects associated with food constituents. The database contains information on more than 28,000 chemicals found in more than 1000 raw or unprocessed food products. The data in FooDB was collected from many sources including textbooks, scientific journals, on-line food composition or nutrient databases, flavour and aroma databases and various on-line metabolomic databases. This literature-derived information has been combined with experimentally derived data measured on thousands of compounds from more than 40 very common food products through the Alberta Food Metabolome Project which is led by David S. Wishart. Users are able to browse through the FooDB data by food source, name, descriptors or function. Chemical structures and molecular weights for compounds in FooDB may be searched via a specialized chemical structure search utility. Users are able to view the content of FooDB using two different “Viewing” options: FoodView, which lists foods by their chemical compounds, or ChemView, which lists chemicals by their food sources. Knowledge about the precise chemical composition of foods can be used to guide public health policies, assist food companies with improved food labelling, help dieticians prepare better dietary plans, support nutraceutical companies with their submissions of health claims and guide consumer choices with regard to food purchases.

The E. coli Metabolome Database (ECMDB) is a freely accessible, online database of small molecule metabolites found in or produced by Escherichia coli. Escherichia coli is perhaps the best studied bacterium on earth and has served as the "model microbe" in microbiology research for more than 60 years. The ECMDB is essentially an E. coli "omics" encyclopedia containing detailed data on the genome, proteome and metabolome of E. coli. ECMDB is part of a suite of organism-specific metabolomics databases that includes DrugBank, HMDB, YMDB and SMPDB. As a metabolomics resource, the ECMDB is designed to facilitate research in the area gut/microbiome metabolomics and environmental metabolomics. The ECMDB contains two kinds of data: 1) chemical data and 2) molecular biology and/or biochemical data. The chemical data includes more than 2700 metabolite structures with detailed metabolite descriptions along with nearly 5000 NMR, GC-MS and LC-MS spectra corresponding to these metabolites. The biochemical data includes nearly 1600 protein sequences and more than 3100 biochemical reactions that are linked to these metabolite entries. Each metabolite entry in the ECMDB contains more than 80 data fields with approximately 65% of the information being devoted to chemical data and the other 35% of the information devoted to enzymatic or biochemical data. Many data fields are hyperlinked to other databases. The ECMDB also has a variety of structure and pathway viewing applets. The ECMDB database offers a number of text, sequence, spectral, chemical structure and relational query searches. These are described in more detail below.

<span class="mw-page-title-main">MetaboLights</span> Metabolomics database

MetaboLights is a data repository founded in 2012 for cross-species and cross-platform metabolomic studies that provides primary research data and meta data for metabolomic studies as well as a knowledge base for properties of individual metabolites. The database is maintained by the European Bioinformatics Institute (EMBL-EBI) and the development is funded by Biotechnology and Biological Sciences Research Council (BBSRC). As of July 2018, the MetaboLights browse functionality consists of 383 studies, two analytical platforms, NMR spectroscopy and mass spectrometry.

<span class="mw-page-title-main">4-Methoxyestrone</span> Chemical compound

4-Methoxyestrone (4-ME1) is an endogenous, naturally occurring methoxylated catechol estrogen and metabolite of estrone that is formed by catechol O-methyltransferase via the intermediate 4-hydroxyestrone. It has estrogenic activity similarly to estrone and 4-hydroxyestrone.

2-Decanone is a ketone with the chemical formula C10H20O.

References

  1. 1 2 "Wishart, Dr. David – ASTech Foundation" . Retrieved 2022-04-08.
  2. 1 2 "David Wishart - Directory@UAlberta". apps.ualberta.ca. Retrieved 2022-04-08.
  3. 1 2 "Honorary Fellowships – Metabolomics Society" . Retrieved 2022-04-08.
  4. ↑ "People". The Metabolomics Innovation Center. Retrieved 2022-04-08.
  5. 1 2 "Chenomx Inc | Metabolite Discovery and Measurement" . Retrieved 2022-04-08.
  6. ↑ "DrugBank | About DrugBank". www.drugbank.com. Retrieved 2022-04-08.
  7. ↑ "Our Team – Molecular You". molecularyou.com. Retrieved 2022-04-08.
  8. ↑ "David Wishart". scholar.google.com. Retrieved 2022-04-08.
  9. ↑ Ioannidis, John P. A.; Baas, Jeroen; Klavans, Richard; Boyack, Kevin W. (2019-08-12). "A standardized citation metrics author database annotated for scientific field". PLOS Biology. 17 (8): e3000384. doi: 10.1371/journal.pbio.3000384 . ISSN   1545-7885. PMC   6699798 . PMID   31404057.
  10. ↑ Baas, Jeroen; Boyack, Kevin; Ioannidis, John P. A. (19 October 2021). "August 2021 data-update for "Updated science-wide author databases of standardized citation indicators"". 3. Elsevier BV. doi:10.17632/btchxktzyw.3 . Retrieved 17 December 2022.{{cite journal}}: Cite journal requires |journal= (help)
  11. ↑ "Highly Cited Researchers". publons.com. Retrieved 2022-04-08.
  12. ↑ "Brian Sykes | Biochemistry". www.ualberta.ca. Retrieved 2022-04-08.
  13. ↑ "University of Alberta Distinguished Professor | Office of the Provost and Vice-President (Academic)". www.ualberta.ca. Retrieved 2022-04-08.
  14. ↑ Wishart, D. S.; Sykes, B. D.; Richards, F. M. (1992-02-18). "The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy". Biochemistry. 31 (6): 1647–1651. doi:10.1021/bi00121a010. ISSN   0006-2960. PMID   1737021.
  15. ↑ Wishart, DavidS.; Sykes, BrianD. (March 1994). "The 13C Chemical-Shift Index: A simple method for the identification of protein secondary structure using 13C chemical-shift data". Journal of Biomolecular NMR. 4 (2): 171–180. doi:10.1007/BF00175245. ISSN   0925-2738. PMID   8019132. S2CID   42323147.
  16. ↑ Berjanskii, Mark V.; Wishart, David S. (2005-11-01). "A Simple Method To Predict Protein Flexibility Using Secondary Chemical Shifts". Journal of the American Chemical Society. 127 (43): 14970–14971. doi:10.1021/ja054842f. ISSN   0002-7863. PMID   16248604.
  17. ↑ Berjanskii, Mark V.; Wishart, David S. (January 2008). "Application of the random coil index to studying protein flexibility". Journal of Biomolecular NMR. 40 (1): 31–48. doi:10.1007/s10858-007-9208-0. ISSN   0925-2738. PMID   17985196. S2CID   40798448.
  18. ↑ Berjanskii, M. V.; Neal, S.; Wishart, D. S. (2006-07-01). "PREDITOR: a web server for predicting protein torsion angle restraints". Nucleic Acids Research. 34 (Web Server): W63–W69. doi:10.1093/nar/gkl341. ISSN   0305-1048. PMC   1538894 . PMID   16845087.
  19. 1 2 Hafsa, Noor E.; Wishart, David S. (November 2014). "CSI 2.0: a significantly improved version of the Chemical Shift Index". Journal of Biomolecular NMR. 60 (2–3): 131–146. doi:10.1007/s10858-014-9863-x. ISSN   0925-2738. PMID   25273503. S2CID   24871947.
  20. ↑ Hafsa, Noor E.; Arndt, David; Wishart, David S. (2015-07-01). "CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts". Nucleic Acids Research. 43 (W1): W370–W377. doi:10.1093/nar/gkv494. ISSN   0305-1048. PMC   4489240 . PMID   25979265.
  21. ↑ Berjanskii, M.; Tang, P.; Liang, J.; Cruz, J. A.; Zhou, J.; Zhou, Y.; Bassett, E.; MacDonell, C.; Lu, P.; Lin, G.; Wishart, D. S. (2009-07-01). "GeNMR: a web server for rapid NMR-based protein structure determination". Nucleic Acids Research. 37 (Web Server): W670–W677. doi:10.1093/nar/gkp280. ISSN   0305-1048. PMC   2703936 . PMID   19406927.
  22. ↑ Wishart, D. S.; Arndt, D.; Berjanskii, M.; Tang, P.; Zhou, J.; Lin, G. (2008-05-19). "CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data". Nucleic Acids Research. 36 (Web Server): W496–W502. doi:10.1093/nar/gkn305. ISSN   0305-1048. PMC   2447725 . PMID   18515350.
  23. ↑ Hafsa, Noor E.; Berjanskii, Mark V.; Arndt, David; Wishart, David S. (January 2018). "Rapid and reliable protein structure determination via chemical shift threading". Journal of Biomolecular NMR. 70 (1): 33–51. doi:10.1007/s10858-017-0154-1. ISSN   0925-2738. PMID   29196969. S2CID   3495790.
  24. ↑ Neal, Stephen (2003). "Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts". Journal of Biomolecular NMR. 26 (3): 215–240. doi:10.1023/A:1023812930288. PMID   12766419. S2CID   29425090.
  25. ↑ Han, Beomsoo; Liu, Yifeng; Ginzinger, Simon W.; Wishart, David S. (May 2011). "SHIFTX2: significantly improved protein chemical shift prediction". Journal of Biomolecular NMR. 50 (1): 43–57. doi:10.1007/s10858-011-9478-4. ISSN   0925-2738. PMC   3085061 . PMID   21448735.
  26. ↑ Wang, Bowei; Wang, Yunjun; Wishart, David S. (June 2010). "A probabilistic approach for validating protein NMR chemical shift assignments". Journal of Biomolecular NMR. 47 (2): 85–99. doi:10.1007/s10858-010-9407-y. ISSN   0925-2738. PMID   20446018. S2CID   22564072.
  27. ↑ Zhang, Haiyan; Neal, Stephen; Wishart, David S. (2003). "RefDB: A database of uniformly referenced protein chemical shifts". Journal of Biomolecular NMR. 25 (3): 173–195. doi:10.1023/A:1022836027055. PMID   12652131. S2CID   12786364.
  28. ↑ "Compound Identification and Quantitation in Liquid Mixtures -- Method and Process Using an Automated Nuclear Magnetic Resonance Measurement System". Canadian Patent Database.
  29. ↑ Wishart, David S (June 2007). "Proteomics and the Human Metabolome Project". Expert Review of Proteomics. 4 (3): 333–335. doi:10.1586/14789450.4.3.333. ISSN   1478-9450. PMID   17552914. S2CID   9501798.
  30. ↑ Wishart, David S; Guo, AnChi; Oler, Eponine; Wang, Fei; Anjum, Afia; Peters, Harrison; Dizon, Raynard; Sayeeda, Zinat; Tian, Siyang; Lee, Brian L; Berjanskii, Mark (2022-01-07). "HMDB 5.0: the Human Metabolome Database for 2022". Nucleic Acids Research. 50 (D1): D622–D631. doi:10.1093/nar/gkab1062. ISSN   0305-1048. PMC   8728138 . PMID   34986597.
  31. 1 2 Law, Vivian; Knox, Craig; Djoumbou, Yannick; Jewison, Tim; Guo, An Chi; Liu, Yifeng; Maciejewski, Adam; Arndt, David; Wilson, Michael; Neveu, Vanessa; Tang, Alexandra (January 2014). "DrugBank 4.0: shedding new light on drug metabolism". Nucleic Acids Research. 42 (D1): D1091–D1097. doi:10.1093/nar/gkt1068. ISSN   0305-1048. PMC   3965102 . PMID   24203711.
  32. 1 2 Scalbert, Augustin; Andres-Lacueva, Cristina; Arita, Masanori; Kroon, Paul; Manach, Claudine; Urpi-Sarda, Mireia; Wishart, David (2011-05-11). "Databases on Food Phytochemicals and Their Health-Promoting Effects". Journal of Agricultural and Food Chemistry. 59 (9): 4331–4348. doi:10.1021/jf200591d. ISSN   0021-8561. PMID   21438636.
  33. ↑ Wishart, David S; Guo, AnChi; Oler, Eponine; Wang, Fei; Anjum, Afia; Peters, Harrison; Dizon, Raynard; Sayeeda, Zinat; Tian, Siyang; Lee, Brian L; Berjanskii, Mark (2022-01-07). "HMDB 5.0: the Human Metabolome Database for 2022". Nucleic Acids Research. 50 (D1): D622–D631. doi:10.1093/nar/gkab1062. ISSN   0305-1048. PMC   8728138 . PMID   34986597.
  34. ↑ Wishart, David; Arndt, David; Pon, Allison; Sajed, Tanvir; Guo, An Chi; Djoumbou, Yannick; Knox, Craig; Wilson, Michael; Liang, Yongjie; Grant, Jason; Liu, Yifeng (2015-01-28). "T3DB: the toxic exposome database". Nucleic Acids Research. 43 (D1): D928–D934. doi:10.1093/nar/gku1004. ISSN   1362-4962. PMC   4383875 . PMID   25378312.
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