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Kim SK, Lee MK, Jang H, Lee JJ, Lee S, Jang Y, Jang H, Kim A. TM-MC 2.0: an enhanced chemical database of medicinal materials in Northeast Asian traditional medicine. BMC Complement Med Ther 2024; 24:40. [PMID: 38229051 PMCID: PMC10790428 DOI: 10.1186/s12906-023-04331-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/26/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND As chromatographic techniques have advanced, many articles that analyze the constituting compounds of medicinal materials have been published in relation to Northeast Asian traditional medicine, including traditional Chinese medicine (TCM). TM-MC was launched in 2015, providing information about the chemical compounds in medicinal materials from chromatographic articles in PubMed. Since 2015, through continuous curation efforts, we have now released TM-MC 2.0 with significant improvements to the quantity and quality of the data ( https://tm-mc.kr ). DESCRIPTION TM-MC 2.0 contains 635 medicinal materials, 34,107 chemical compounds (21,306 identified and de-duplicated), 13,992 targets, 27,997 diseases, and 5,075 prescriptions (2,393 de-duplicated by name). The database provides the largest number of identified compounds for medicinal materials listed in the pharmacopoeia compared to all TCM databases. In particular, marker compounds of medicinal materials and many newly discovered compounds were added through the manual curation of recent chromatographic articles. CONCLUSION TM-MC 2.0 provides the largest collection of information about the chemical compounds of the medicinal materials listed in the Korean, Chinese, and Japanese pharmacopoeias. Our database can be utilized for network pharmacology in traditional medicine and for the compound screening of medicinal materials for modern drug discovery.
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Affiliation(s)
- Sang-Kyun Kim
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea.
| | - Myung-Ku Lee
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
| | - Ho Jang
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
| | - Jeong-Ju Lee
- Division of Biomedical Research, Korea Research Institute of Bioscience & Biotechnology, Daejeon, 34141, Republic of Korea
| | - Sanghun Lee
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
| | - Yunji Jang
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
| | - Hyunchul Jang
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
| | - Anna Kim
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea
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Frey JG, Hartshorn RM, McEwen LR. Specification of International Chemical Identifier (InChI) QR codes for linking labels on containers of chemical samples to digital resources (IUPAC Recommendations 2021). PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Abstract
This article discusses the ways of linking physical objects to digital information relevant to chemical entities, specifically those that can be described by the use of the IUPAC International Chemical Identifier (InChI). It makes recommendations on the form of the computer readable components of labels provided for chemicals and materials that are used on product/sample containers and on the associated documentation that is used when transporting these containers (either internally or during export/import). The focus is on specification of the content of the 2D Quick Response bar codes required to describe the molecular content of the containers and link to digital resources to supplement that provided on a physical label. The necessary technical and (possible) business infrastructure necessary to support the use of the InChI and InChIKey for rapid recall of relevant information is considered here and suggestions are made.
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Affiliation(s)
- Jeremy G. Frey
- School of Chemistry, University of Southampton , Highfield , Southampton , SO17 1BJ , UK
| | - Richard M. Hartshorn
- School of Physical and Chemical Sciences, University of Canterbury , Christchurch , New Zealand
| | - Leah R. McEwen
- Physical Sciences Library, Cornell University , 293 Clark Hall , Ithaca , NY 14853-2501 , USA
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Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis. Int J Mol Sci 2022; 23:ijms232012351. [PMID: 36293229 PMCID: PMC9604016 DOI: 10.3390/ijms232012351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.
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Paulhe N, Canlet C, Damont A, Peyriga L, Durand S, Deborde C, Alves S, Bernillon S, Berton T, Bir R, Bouville A, Cahoreau E, Centeno D, Costantino R, Debrauwer L, Delabrière A, Duperier C, Emery S, Flandin A, Hohenester U, Jacob D, Joly C, Jousse C, Lagree M, Lamari N, Lefebvre M, Lopez-Piffet C, Lyan B, Maucourt M, Migne C, Olivier MF, Rathahao-Paris E, Petriacq P, Pinelli J, Roch L, Roger P, Roques S, Tabet JC, Tremblay-Franco M, Traïkia M, Warnet A, Zhendre V, Rolin D, Jourdan F, Thévenot E, Moing A, Jamin E, Fenaille F, Junot C, Pujos-Guillot E, Giacomoni F. PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management. Metabolomics 2022; 18:40. [PMID: 35699774 PMCID: PMC9197906 DOI: 10.1007/s11306-022-01899-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/22/2022] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .
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Affiliation(s)
- Nils Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cécile Canlet
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Annelaure Damont
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Lindsay Peyriga
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077, Toulouse, France
| | - Stéphanie Durand
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Catherine Deborde
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Sandra Alves
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Stephane Bernillon
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Thierry Berton
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Raphael Bir
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Alyssa Bouville
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Edern Cahoreau
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077, Toulouse, France
| | - Delphine Centeno
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Robin Costantino
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Laurent Debrauwer
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Alexis Delabrière
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Christophe Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Sylvain Emery
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Amelie Flandin
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Ulli Hohenester
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Daniel Jacob
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Charlotte Joly
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cyril Jousse
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marie Lagree
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nadia Lamari
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Marie Lefebvre
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Claire Lopez-Piffet
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Bernard Lyan
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Mickael Maucourt
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Carole Migne
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marie-Francoise Olivier
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Estelle Rathahao-Paris
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Pierre Petriacq
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Julie Pinelli
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Léa Roch
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Pierrick Roger
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Simon Roques
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Jean-Claude Tabet
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Marie Tremblay-Franco
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Mounir Traïkia
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Anna Warnet
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Vanessa Zhendre
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Dominique Rolin
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Fabien Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Etienne Thévenot
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Annick Moing
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Emilien Jamin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - François Fenaille
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Christophe Junot
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Franck Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
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Nietert MM, Vinhoven L, Auer F, Hafkemeyer S, Stanke F. Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR. Front Pharmacol 2021; 12:689205. [PMID: 34955819 PMCID: PMC8692862 DOI: 10.3389/fphar.2021.689205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 11/08/2021] [Indexed: 01/02/2023] Open
Abstract
Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR, which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics.
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Affiliation(s)
- Manuel Manfred Nietert
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.,CIDAS Campus Institute Data Science, Georg-August-University, Göttingen, Germany
| | - Liza Vinhoven
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Florian Auer
- Institute for Informatics, University of Augsburg, Augsburg, Germany
| | | | - Frauke Stanke
- German Center for Lung Research (DZL), Partner Site BREATH, Hannover, Germany.,Clinic for Pediatric Pneumology, Allergology, and Neonatology, Hannover Medical School, Hannover, Germany
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Beniddir MA, Kang KB, Genta-Jouve G, Huber F, Rogers S, van der Hooft JJJ. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 2021; 38:1967-1993. [PMID: 34821250 PMCID: PMC8597898 DOI: 10.1039/d1np00023c] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
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Affiliation(s)
- Mehdi A Beniddir
- Université Paris-Saclay, CNRS, BioCIS, 5 rue J.-B Clément, 92290 Châtenay-Malabry, France
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Grégory Genta-Jouve
- Laboratoire de Chimie-Toxicologie Analytique et Cellulaire (C-TAC), UMR CNRS 8038, CiTCoM, Université de Paris, 4, Avenue de l'Observatoire, 75006, Paris, France
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 275 Route de Montabo, 97334 Cayenne, French Guiana, France
| | - Florian Huber
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
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Škuta C, Southan C, Bartůněk P. Will the chemical probes please stand up? RSC Med Chem 2021; 12:1428-1441. [PMID: 34447939 PMCID: PMC8372204 DOI: 10.1039/d1md00138h] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/22/2022] Open
Abstract
In 2005, the NIH Molecular Libraries Program (MLP) undertook the identification of tool compounds to expand biological insights, now termed small-molecule chemical probes. This inspired other organisations to initiate similar efforts from 2010 onwards. As a central focus of the Probes & Drugs portal (P&D), we have standardised, integrated and compared sets of declared probe compounds harvested from 12 different sources. This turned out to be challenging and revealed unexpected anomalies. Results in this work address key questions including; a) individual and total structure counts, b) overlaps between sources, c) comparisons with selected PubChem sources and d) investigating the probe coverage of druggable targets. In addition, we developed new high-level scoring schemes to filter collections down to probes of higher quality. This generated 548 high-quality chemical probes (HQCP) covering 447 distinct protein targets. This HQCP collection has been added to the P&D portal and will be regularly updated as established sources expand and new ones release data.
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Affiliation(s)
- Ctibor Škuta
- CZ-OPENSCREEN, National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences Vídeňská 1083 142 20 Prague 4 Czech Republic
| | - Christopher Southan
- Deanery of Biomedical Sciences, University of Edinburgh Edinburgh EH8 9XD UK
| | - Petr Bartůněk
- CZ-OPENSCREEN, National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences Vídeňská 1083 142 20 Prague 4 Czech Republic
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Lynch I, Afantitis A, Exner T, Himly M, Lobaskin V, Doganis P, Maier D, Sanabria N, Papadiamantis AG, Rybinska-Fryca A, Gromelski M, Puzyn T, Willighagen E, Johnston BD, Gulumian M, Matzke M, Green Etxabe A, Bossa N, Serra A, Liampa I, Harper S, Tämm K, Jensen ACØ, Kohonen P, Slater L, Tsoumanis A, Greco D, Winkler DA, Sarimveis H, Melagraki G. Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies? NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2493. [PMID: 33322568 PMCID: PMC7764592 DOI: 10.3390/nano10122493] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/05/2020] [Accepted: 12/08/2020] [Indexed: 12/16/2022]
Abstract
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.
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Affiliation(s)
- Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Thomas Exner
- Edelweiss Connect GmbH, Hochbergerstrasse 60C, 4057 Basel, Switzerland;
| | - Martin Himly
- Department Biosciences, Paris Lodron University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria;
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland;
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany;
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa; (N.S.); (M.G.)
| | - Anastasios G. Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Anna Rybinska-Fryca
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Maciej Gromelski
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Egon Willighagen
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands;
| | - Blair D. Johnston
- Department Chemicals and Product Safety, Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, 10589 Berlin, Germany;
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine, University of the Witwatersrand, 1 Jan Smuts Ave, Johannesburg 2000, South Africa
| | - Marianne Matzke
- UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford OX10 8BB, UK; (M.M.); (A.G.E.)
| | - Amaia Green Etxabe
- UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford OX10 8BB, UK; (M.M.); (A.G.E.)
| | - Nathan Bossa
- LEITAT Technological Center, Circular Economy Business Unit, C/de La Innovació 2, 08225 Terrassa, Barcelona, Spain;
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (D.G.)
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Stacey Harper
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, 116 Johnson Hall 105 SW 26th St., Corvallis, OR 97331, USA;
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia;
| | - Alexander CØ Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark;
| | - Pekka Kohonen
- Misvik Biology OY, Karjakatu 35 B, 20520 Turku, Finland;
| | - Luke Slater
- Institute of Cancer and Genomics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Andreas Tsoumanis
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (D.G.)
| | - David A. Winkler
- Institute of Molecular Sciences, La Trobe University, Kingsbury Drive, Bundoora 3086, Australia;
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK
- CSIRO Data61, Pullenvale 4069, Australia
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
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9
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David L, Thakkar A, Mercado R, Engkvist O. Molecular representations in AI-driven drug discovery: a review and practical guide. J Cheminform 2020; 12:56. [PMID: 33431035 PMCID: PMC7495975 DOI: 10.1186/s13321-020-00460-5] [Citation(s) in RCA: 165] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 09/05/2020] [Indexed: 02/08/2023] Open
Abstract
The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.
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Affiliation(s)
- Laurianne David
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, Astrazeneca Gothenburg, Sweden.
| | - Amol Thakkar
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, Astrazeneca Gothenburg, Sweden
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Rocío Mercado
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, Astrazeneca Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, Astrazeneca Gothenburg, Sweden
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10
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Southan C. Opening up connectivity between documents, structures and bioactivity. Beilstein J Org Chem 2020; 16:596-606. [PMID: 32280387 PMCID: PMC7136548 DOI: 10.3762/bjoc.16.54] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/12/2020] [Indexed: 12/17/2022] Open
Abstract
Bioscientists reading papers or patents strive to discern the key relationships reported within a document "D" where a bioactivity "A" with a quantitative result "R" (e.g., an IC50) is reported for chemical structure "C" that modulates (e.g., inhibits) a protein target "P". A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.
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Affiliation(s)
- Christopher Southan
- Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
- TW2Informatics Ltd, Västra Frölunda, Gothenburg, 42166, Sweden
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11
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Southan C. Caveat Usor: Assessing Differences between Major Chemistry Databases. ChemMedChem 2018; 13:470-481. [PMID: 29451740 PMCID: PMC5900829 DOI: 10.1002/cmdc.201700724] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 02/07/2018] [Indexed: 12/24/2022]
Abstract
The three databases of PubChem, ChemSpider, and UniChem capture the majority of open chemical structure records with February 2018 totals of 95, 63, and 154 million, respectively. Collectively, they constitute a massively enabling resource for cheminformatics, chemical biology, and drug discovery. As meta-portals, they subsume and link out to the major proportion of public bioactivity data extracted from the literature and screening center assay results. Therefore, they not only present three different entry points, but the many subsumed independent resources present a fourth entry point in the form of standalone databases. Because this creates a complex picture it is important for users to have at least some appreciation of differential content to enable utility judgments for the tasks at hand. This turns out to be challenging. By comparing the three resources in detail, this review assesses their differences, some of which are not obvious. This includes the fact that coverage is significantly different between the 587, 282, and 38 contributing sources, respectively. This not only presents the "who-has-what" question, but also the reason "why" any particular inclusion is considered valuable is rarely made explicit. Also confusing is that sources nominally in common (i.e., having the same submitter name) can have significantly different structure counts, not only in each of the three but also from their standalone instantiations. Assessing a series of examples indicates that differences in loading dates and structural standardization are the main causes of this inter-portal discordance.
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Affiliation(s)
- Christopher Southan
- IUPHAR/BPS Guide to PHARMACOLOGY, Deanery of Biomedical SciencesUniversity of EdinburghEdinburghEH8 9XDUK
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12
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Minkiewicz P, Iwaniak A, Darewicz M. Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions. Molecules 2017; 22:molecules22122075. [PMID: 29186902 PMCID: PMC6149970 DOI: 10.3390/molecules22122075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/15/2017] [Accepted: 11/25/2017] [Indexed: 12/20/2022] Open
Abstract
Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.
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Affiliation(s)
- Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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13
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Milton J, Zhang T, Bellamy C, Swayze E, Hart C, Weisser M, Hecht S, Rotstein S. HELM Software for Biopolymers. J Chem Inf Model 2017; 57:1233-1239. [PMID: 28471655 DOI: 10.1021/acs.jcim.6b00442] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Hierarchical Editing Language for Macromolecules (HELM version 2.0) is a molecular line notation similar to SMILEs but specifically for communicating and managing biopolymer structures. The HELM project, part of the Pistoia Alliance nonprofit organization, has been tasked to develop and promote HELM as a global exchange format and recently released version 2.0 of the specification. Here we will describe the specifics of the HELM v2.0 notation along with the large ecosystem of software to support HELM-based structure management. We will highlight a recent open-source software and database for HELM monomers and a new, simpler approach to deploying a large complicated molecular management system.
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Affiliation(s)
- Jeff Milton
- Ionis Pharmaceuticals, Inc , 2855 Gazelle Court, Carlsbad, California 92010, United States
| | - Tianhong Zhang
- Pfizer Inc. , One Burtt Road, Andover, Massachusetts 01810, United States
| | - Claire Bellamy
- Pistoia Alliance , 401 Edgewater Place, Wakefield, Massachusetts 01880-6201, United States
| | - Eric Swayze
- Ionis Pharmaceuticals, Inc , 2855 Gazelle Court, Carlsbad, California 92010, United States
| | - Christopher Hart
- Ionis Pharmaceuticals, Inc , 2855 Gazelle Court, Carlsbad, California 92010, United States
| | - Markus Weisser
- Quattro Research , Fraunhoferstraße 18a 82152 Planegg-Martinsried, Germany
| | - Sabrina Hecht
- Quattro Research , Fraunhoferstraße 18a 82152 Planegg-Martinsried, Germany
| | - Sergio Rotstein
- Pfizer Inc. , One Burtt Road, Andover, Massachusetts 01810, United States
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14
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Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A. Information Retrieval and Text Mining Technologies for Chemistry. Chem Rev 2017; 117:7673-7761. [PMID: 28475312 DOI: 10.1021/acs.chemrev.6b00851] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre , C/Melchor Fernández Almagro 3, Madrid E-28029, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Anália Lourenço
- ESEI - Department of Computer Science, University of Vigo , Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense E-32004, Spain.,Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia) , Campus Universitario Lagoas-Marcosende, Vigo E-36310, Spain.,CEB-Centre of Biological Engineering, University of Minho , Campus de Gualtar, Braga 4710-057, Portugal
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Alfonso Valencia
- Life Science Department, Barcelona Supercomputing Centre (BSC-CNS) , C/Jordi Girona, 29-31, Barcelona E-08034, Spain.,Joint BSC-IRB-CRG Program in Computational Biology, Parc Científic de Barcelona , C/ Baldiri Reixac 10, Barcelona E-08028, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig de Lluís Companys 23, Barcelona E-08010, Spain
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15
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Minkiewicz P, Darewicz M, Iwaniak A, Bucholska J, Starowicz P, Czyrko E. Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science. Int J Mol Sci 2016; 17:ijms17122039. [PMID: 27929431 PMCID: PMC5187839 DOI: 10.3390/ijms17122039] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Revised: 11/17/2016] [Accepted: 11/29/2016] [Indexed: 01/02/2023] Open
Abstract
Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.
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Affiliation(s)
- Piotr Minkiewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Justyna Bucholska
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Piotr Starowicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Emilia Czyrko
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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16
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Wohlgemuth G, Mehta SS, Mejia RF, Neumann S, Pedrosa D, Pluskal T, Schymanski EL, Willighagen EL, Wilson M, Wishart DS, Arita M, Dorrestein PC, Bandeira N, Wang M, Schulze T, Salek RM, Steinbeck C, Nainala VC, Mistrik R, Nishioka T, Fiehn O. SPLASH, a hashed identifier for mass spectra. Nat Biotechnol 2016; 34:1099-1101. [PMID: 27824832 PMCID: PMC5515539 DOI: 10.1038/nbt.3689] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Gert Wohlgemuth
- West Coast Metabolomics Center and Genome Center University of California Davis, Davis, CA, USA
| | - Sajjan S. Mehta
- West Coast Metabolomics Center and Genome Center University of California Davis, Davis, CA, USA
| | - Ramon F. Mejia
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Diego Pedrosa
- West Coast Metabolomics Center and Genome Center University of California Davis, Davis, CA, USA
| | - Tomáš Pluskal
- Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA, USA
| | - Emma L. Schymanski
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands
| | - Michael Wilson
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - David S. Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
- National Institute of Genetics, Mishima, Shizuoka, Japan
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, CA, USA
- Departments of Pharmacology and Pediatrics, School of Medicine, UC San Diego, La Jolla, CA, USA
| | - Nuno Bandeira
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, CA, USA
- Computer Science and Engineering, UC San Diego, La Jolla, CA, USA
- Center for Computational Mass Spectrometry, UC San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Computer Science and Engineering, UC San Diego, La Jolla, CA, USA
- Center for Computational Mass Spectrometry, UC San Diego, La Jolla, CA, USA
| | - Tobias Schulze
- Department of Effect-Directed Analysis, UFZ Helmholtz Centre for Environmental Research GmbH, Leipzig, Germany
| | - Reza M. Salek
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Christoph Steinbeck
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Venkata Chandrasekhar Nainala
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Takaaki Nishioka
- Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Kyoto, Japan
| | - Oliver Fiehn
- West Coast Metabolomics Center and Genome Center University of California Davis, Davis, CA, USA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
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17
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Smith C, Todd M, Patiny L, Swain C, Southan C, Williamson A, Clark A. SCINDR - The SCience INtroDuction Robot that will Connect Open Scientists. RESEARCH IDEAS AND OUTCOMES 2016. [DOI: 10.3897/rio.2.e9995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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18
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Iwaniak A, Minkiewicz P, Darewicz M, Sieniawski K, Starowicz P. BIOPEP database of sensory peptides and amino acids. Food Res Int 2016; 85:155-161. [PMID: 29544830 DOI: 10.1016/j.foodres.2016.04.031] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 04/22/2016] [Accepted: 04/24/2016] [Indexed: 11/29/2022]
Abstract
Peptides and amino acids belong to compounds that influence the taste of foods. The aim of this study was to develop a database of sensory peptides and amino acids. Information about the taste of the analyzed compounds was obtained from sensory studies described in the literature. The database of sensory peptides and amino acids has identical structure to the BIOPEP database of biologically active peptides. The information about sensory peptides and amino acids was inserted into the database using standard BIOPEP layouts for bioactive peptides. Information about the biological activity of sensory peptides was obtained from BIOPEP and other databases. The information annotated in the BIOPEP database of sensory peptides and amino acids includes: sequence written in a one-letter code, information about taste, reference, structure written with the use of chemical codes (SMILES, InChI and InChIKey), bioactivity data (mainly inhibition of proteolytic enzymes), if applicable, and ID numbers from other biological and chemical databases. The database contains tools for determining the location of peptides in protein sequences (profiles of potential sensory activity), comparing protein sequences as precursors of sensory peptides based on the frequency of sensory fragments as a quantitative descriptor, simulating proteolysis and calculating novel parameters for quantitative description of simulated proteolysis. The BIOPEP database of sensory peptides and amino acids is available at http://www.uwm.edu.pl/biochemia/index.php/pl/biopep. It is an open access resource that does not require user registration.
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Affiliation(s)
- Anna Iwaniak
- University of Warmia and Mazury in Olsztyn, Department of Food Biochemistry, Plac Cieszyński 1, 10-726 Olsztyn, Poland
| | - Piotr Minkiewicz
- University of Warmia and Mazury in Olsztyn, Department of Food Biochemistry, Plac Cieszyński 1, 10-726 Olsztyn, Poland.
| | - Małgorzata Darewicz
- University of Warmia and Mazury in Olsztyn, Department of Food Biochemistry, Plac Cieszyński 1, 10-726 Olsztyn, Poland
| | - Krzysztof Sieniawski
- Enter Krzysztof Sieniawski, ul. Stanisława Żurawskiego 17/19, 10-689 Olsztyn, Poland
| | - Piotr Starowicz
- University of Warmia and Mazury in Olsztyn, Department of Food Biochemistry, Plac Cieszyński 1, 10-726 Olsztyn, Poland
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Southan C, Sharman JL, Benson HE, Faccenda E, Pawson AJ, Alexander SPH, Buneman OP, Davenport AP, McGrath JC, Peters JA, Spedding M, Catterall WA, Fabbro D, Davies JA. The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res 2016; 44:D1054-68. [PMID: 26464438 PMCID: PMC4702778 DOI: 10.1093/nar/gkv1037] [Citation(s) in RCA: 987] [Impact Index Per Article: 123.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 09/25/2015] [Accepted: 09/29/2015] [Indexed: 01/05/2023] Open
Abstract
The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.
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Affiliation(s)
- Christopher Southan
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Joanna L Sharman
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Helen E Benson
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Elena Faccenda
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Adam J Pawson
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Stephen P H Alexander
- School of Biomedical Sciences, University of Nottingham Medical School, Nottingham, NG7 2UH, UK
| | - O Peter Buneman
- Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, Edinburgh, EH8 9LE, UK
| | | | - John C McGrath
- School of Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - John A Peters
- Neuroscience Division, Medical Education Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK
| | | | - William A Catterall
- Department of Pharmacology, University of Washington, Seattle, WA 98195-7280, USA
| | | | - Jamie A Davies
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, EH8 9XD, UK
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Bisson J, McAlpine JB, Friesen JB, Chen SN, Graham J, Pauli GF. Can Invalid Bioactives Undermine Natural Product-Based Drug Discovery? J Med Chem 2015; 59:1671-90. [PMID: 26505758 PMCID: PMC4791574 DOI: 10.1021/acs.jmedchem.5b01009] [Citation(s) in RCA: 171] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
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High-throughput biology has contributed
a wealth of data on chemicals,
including natural products (NPs). Recently, attention was drawn to
certain, predominantly synthetic, compounds that are responsible for
disproportionate percentages of hits but are false actives. Spurious
bioassay interference led to their designation as pan-assay interference
compounds (PAINS). NPs lack comparable scrutiny,
which this study aims to rectify. Systematic mining of 80+ years of
the phytochemistry and biology literature, using the NAPRALERT database,
revealed that only 39 compounds represent the NPs most reported by
occurrence, activity, and distinct activity. Over 50% are not explained
by phenomena known for synthetic libraries, and all had manifold ascribed
bioactivities, designating them as invalid metabolic panaceas (IMPs). Cumulative
distributions of ∼200,000 NPs uncovered that NP research follows
power-law characteristics typical for behavioral phenomena. Projection
into occurrence–bioactivity–effort space produces the
hyperbolic black hole of NPs, where IMPs populate the high-effort
base.
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Affiliation(s)
| | | | - J Brent Friesen
- Physical Sciences Department, Rosary College of Arts and Sciences, Dominican University , River Forest, Illinois 60305, United States
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Minkiewicz P, Darewicz M, Iwaniak A, Sokołowska J, Starowicz P, Bucholska J, Hrynkiewicz M. Common Amino Acid Subsequences in a Universal Proteome--Relevance for Food Science. Int J Mol Sci 2015; 16:20748-73. [PMID: 26340620 PMCID: PMC4613229 DOI: 10.3390/ijms160920748] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 08/18/2015] [Accepted: 08/24/2015] [Indexed: 02/06/2023] Open
Abstract
A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept.
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Affiliation(s)
- Piotr Minkiewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Małgorzata Darewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Anna Iwaniak
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Jolanta Sokołowska
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Piotr Starowicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Justyna Bucholska
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
| | - Monika Hrynkiewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, Olsztyn-Kortowo 10-726, Poland.
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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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Abstract
The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA ; Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, USA
| | - Christopher Southan
- IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, Edinburgh, EH8 9XD, UK
| | - Megan Coffee
- Center for Infectious Diseases and Emergency Readiness, University of California at Berkeley, 1918 University Ave, Berkeley, CA, 94704, USA
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Abstract
The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA ; Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, USA
| | - Christopher Southan
- IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, Edinburgh, EH8 9XD, UK
| | - Megan Coffee
- Center for Infectious Diseases and Emergency Readiness, University of California at Berkeley, 1918 University Ave, Berkeley, CA, 94704, USA
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Expanding opportunities for mining bioactive chemistry from patents. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 14:3-9. [PMID: 26194581 PMCID: PMC4548146 DOI: 10.1016/j.ddtec.2014.12.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 11/12/2014] [Accepted: 12/05/2014] [Indexed: 11/24/2022]
Abstract
Bioactive structures published in medicinal chemistry patents typically exceed those in papers by at least twofold and may precede them by several years. The Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived compounds in PubChem. While mapping between chemical structures, assay results and protein targets from patent documents is challenging, these relationships can be harvested using open tools and are beginning to be curated into databases.
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Lipinski CA, Litterman NK, Southan C, Williams AJ, Clark AM, Ekins S. Parallel worlds of public and commercial bioactive chemistry data. J Med Chem 2014; 58:2068-76. [PMID: 25415348 PMCID: PMC4360371 DOI: 10.1021/jm5011308] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
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The
availability of structures and linked bioactivity data in databases
is powerfully enabling for drug discovery and chemical biology. However,
we now review some confounding issues with the divergent expansions
of public and commercial sources of chemical structures. These are
associated with not only expanding patent extraction but also increasingly
large vendor collections amassed via different selection criteria
between SciFinder from Chemical Abstracts Service (CAS) and major
public sources such as PubChem, ChemSpider, UniChem, and others. These
increasingly massive collections may include both real and virtual
compounds, as well as so-called prophetic compounds from patents.
We address a range of issues raised by the challenges faced resolving
the NIH probe compounds. In addition we highlight the confounding
of prior-art searching by virtual compounds that could impact the
composition of matter patentability of a new medicinal chemistry lead.
Finally, we propose some potential solutions.
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Affiliation(s)
- Christopher A Lipinski
- Christopher A. Lipinski, Ph.D., LLC , 10 Connshire Drive, Waterford, Connecticut 06385-4122, United States
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ROBERTSON MURRAYN, YLIOJA PAULM, WILLIAMSON ALICEE, WOELFLE MICHAEL, ROBINS MICHAEL, BADIOLA KATRINAA, WILLIS PAUL, OLLIARO PIERO, WELLS TIMOTHYNC, TODD MATTHEWH. Open source drug discovery - a limited tutorial. Parasitology 2014; 141:148-57. [PMID: 23985301 PMCID: PMC3884843 DOI: 10.1017/s0031182013001121] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Revised: 06/05/2013] [Accepted: 06/06/2013] [Indexed: 11/22/2022]
Abstract
Open science is a new concept for the practice of experimental laboratory-based research, such as drug discovery. The authors have recently gained experience in how to run such projects and here describe some straightforward steps others may wish to take towards more openness in their own research programmes. Existing and inexpensive online tools can solve many challenges, while some psychological barriers to the free sharing of all data and ideas are more substantial.
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Affiliation(s)
| | - PAUL M. YLIOJA
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - MICHAEL WOELFLE
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - MICHAEL ROBINS
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - KATRINA A. BADIOLA
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - PAUL WILLIS
- Medicines for Malaria Venture, PO Box 1826, 20 rte de Pré-Bois, 1215 Geneva 15, Switzerland
| | - PIERO OLLIARO
- UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland
| | - TIMOTHY N. C. WELLS
- Medicines for Malaria Venture, PO Box 1826, 20 rte de Pré-Bois, 1215 Geneva 15, Switzerland
| | - MATTHEW H. TODD
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
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Tripp RA, Mark Tompkins S. Antiviral effects of inhibiting host gene expression. Curr Top Microbiol Immunol 2014; 386:459-77. [PMID: 25007848 DOI: 10.1007/82_2014_409] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
RNA interference (RNAi) has been used to probe the virus-host interface to understand the requirements for host-gene expression needed for virus replication. The availability of arrayed siRNA libraries has enabled a genome-scale, high-throughput analysis of gene pathways usurped for virus replication. Results from these and related screens have led to the discovery of new host factors that regulate virus replication. While effective delivery continues to limit development of RNAi-based drugs, RNAi-based genome discovery has led to identification of druggable targets. These validated targets enable rational development of novel antiviral drugs, including the rescue and repurposing of existing, approved drugs. Existing drugs with known cytotoxicity and mechanisms of action can potentially be re-targeted to regulate host genes and gene products needed by influenza to replicate. Drug repositioning is more cost-effective, less time-consuming, and more effective for anti-influenza virus drug discovery than traditional methods. In this chapter, a general overview of RNAi screening methods, host-gene discovery, and drug repurposing is examined with emphasis on utilizing RNAi to identify druggable genes that can be targeted for drug development or repurposing.
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Affiliation(s)
- Ralph A Tripp
- Department of Infectious Disease, University of Georgia, Athens, GA, 30602, USA,
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Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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Affiliation(s)
- Gregory Sliwoski
- Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725.
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Southan C, Stracz A. Extracting and connecting chemical structures from text sources using chemicalize.org. J Cheminform 2013; 5:20. [PMID: 23618056 PMCID: PMC3648358 DOI: 10.1186/1758-2946-5-20] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 04/18/2013] [Indexed: 12/04/2022] Open
Abstract
Background Exploring bioactive chemistry requires navigating between structures and data from a variety of text-based sources. While PubChem currently includes approximately 16 million document-extracted structures (15 million from patents) the extent of public inter-document and document-to-database links is still well below any estimated total, especially for journal articles. A major expansion in access to text-entombed chemistry is enabled by chemicalize.org. This on-line resource can process IUPAC names, SMILES, InChI strings, CAS numbers and drug names from pasted text, PDFs or URLs to generate structures, calculate properties and launch searches. Here, we explore its utility for answering questions related to chemical structures in documents and where these overlap with database records. These aspects are illustrated using a common theme of Dipeptidyl Peptidase 4 (DPPIV) inhibitors. Results Full-text open URL sources facilitated the download of over 1400 structures from a DPPIV patent and the alignment of specific examples with IC50 data. Uploading the SMILES to PubChem revealed extensive linking to patents and papers, including prior submissions from chemicalize.org as submitting source. A DPPIV medicinal chemistry paper was completely extracted and structures were aligned to the activity results table, as well as linked to other documents via PubChem. In both cases, key structures with data were partitioned from common chemistry by dividing them into individual new PDFs for conversion. Over 500 structures were also extracted from a batch of PubMed abstracts related to DPPIV inhibition. The drug structures could be stepped through each text occurrence and included some converted MeSH-only IUPAC names not linked in PubChem. Performing set intersections proved effective for detecting compounds-in-common between documents and merged extractions. Conclusion This work demonstrates the utility of chemicalize.org for the exploration of chemical structure connectivity between documents and databases, including structure searches in PubChem, InChIKey searches in Google and the chemicalize.org archive. It has the flexibility to extract text from any internal, external or Web source. It synergizes with other open tools and the application is undergoing continued development. It should thus facilitate progress in medicinal chemistry, chemical biology and other bioactive chemistry domains.
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Abstract
INTRODUCTION When two novel aspartyl proteases were published in 1999 and 2000, beta-site APP-cleaving enzyme 1 (BACE1) was confirmed as the long sought after beta-secretase and Alzheimer's disease drug target. However, the role of its paralogue, BACE2, proved elusive until a 2011 publication implicated it as a Collectrin (TMEM27) secretase controlling pancreatic beta-cell proliferation and a new therapeutic intervention for diabetes. AREAS COVERED This review, using SureChemOpen, encompasses early validation compounds and small-molecule BACE2 inhibitors for diabetes. Since 2010, one assay patent and several chemical series have been published by Roche but these were followed by filings from Novartis and Schering in 2012. The patents from these three companies include BACE2-only filings but also some specifying both BACE1 and BACE2 inhibitors. EXPERT OPINION Roche's early collaborative target validation has given them a lead in BACE2 medicinal chemistry. However, the extensive data output for BACE1 in patents and papers over the last decade, plus liganded crystal structures for both proteases, should expedite the design of BACE2 inhibitors by other organisations. This may also shorten the development time for clinical candidates that, unlike those now entering Phase I trials for BACE1, would not need to be brain-penetrant.
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