John P. Overington
European Bioinformatics Institute
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Publication
Featured researches published by John P. Overington.
Nature Reviews Drug Discovery | 2006
John P. Overington; Bissan Al-Lazikani; Andrew L. Hopkins
For the past decade, the number of molecular targets for approved drugs has been debated. Here, we reconcile apparently contradictory previous reports into a comprehensive survey, and propose a consensus number of current drug targets for all classes of approved therapeutic drugs. One striking feature is the relatively constant historical rate of target innovation (the rate at which drugs against new targets are launched); however, the rate of developing drugs against new families is significantly lower. The recent approval of drugs that target protein kinases highlights two additional trends: an emerging realization of the importance of polypharmacology, and also the power of a gene-family-led approach in generating novel and important therapies.
Nucleic Acids Research | 2012
Anna Gaulton; Louisa J. Bellis; A. Patrícia Bento; Jon Chambers; Mark Davies; Anne Hersey; Yvonne Light; Shaun McGlinchey; David Michalovich; Bissan Al-Lazikani; John P. Overington
ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.
Nature | 2009
Matthew Berriman; Brian J. Haas; Philip T. LoVerde; R. Alan Wilson; Gary P. Dillon; Gustavo C. Cerqueira; Susan T. Mashiyama; Bissan Al-Lazikani; Luiza F. Andrade; Peter D. Ashton; Martin Aslett; Daniella Castanheira Bartholomeu; Gaëlle Blandin; Conor R. Caffrey; Avril Coghlan; Richard M. R. Coulson; Tim A. Day; Arthur L. Delcher; Ricardo DeMarco; Appoliniare Djikeng; Tina Eyre; John Gamble; Elodie Ghedin; Yong-Hong Gu; Christiane Hertz-Fowler; Hirohisha Hirai; Yuriko Hirai; Robin Houston; Alasdair Ivens; David A. Johnston
Schistosoma mansoni is responsible for the neglected tropical disease schistosomiasis that affects 210 million people in 76 countries. Here we present analysis of the 363 megabase nuclear genome of the blood fluke. It encodes at least 11,809 genes, with an unusual intron size distribution, and new families of micro-exon genes that undergo frequent alternative splicing. As the first sequenced flatworm, and a representative of the Lophotrochozoa, it offers insights into early events in the evolution of the animals, including the development of a body pattern with bilateral symmetry, and the development of tissues into organs. Our analysis has been informed by the need to find new drug targets. The deficits in lipid metabolism that make schistosomes dependent on the host are revealed, and the identification of membrane receptors, ion channels and more than 300 proteases provide new insights into the biology of the life cycle and new targets. Bioinformatics approaches have identified metabolic chokepoints, and a chemogenomic screen has pinpointed schistosome proteins for which existing drugs may be active. The information generated provides an invaluable resource for the research community to develop much needed new control tools for the treatment and eradication of this important and neglected disease.
Nucleic Acids Research | 2014
A. Patrícia Bento; Anna Gaulton; Anne Hersey; Louisa J. Bellis; Jon Chambers; Mark Davies; Felix A. Kruger; Yvonne Light; Lora Mak; Shaun McGlinchey; Michał Nowotka; George Papadatos; Rita Santos; John P. Overington
ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.
Bioinformatics | 1998
Kenji Mizuguchi; Charlotte M. Deane; Tom L. Blundell; Mark S. Johnson; John P. Overington
MOTIVATION JOY is a program to annotate protein sequence alignments with three-dimensional (3D) structural features. It was developed to display 3D structural information in a sequence alignment and to help understand the conservation of amino acids in their specific local environments. RESULTS : The JOY representation now constitutes an essential part of the two databases of protein structure alignments: HOMSTRAD (http://www-cryst.bioc.cam.ac.uk/homstrad ) and CAMPASS (http://www-cryst.bioc.cam.ac. uk/campass). It has also been successfully used for identifying distant evolutionary relationships. AVAILABILITY The program can be obtained via anonymous ftp from torsa.bioc.cam.ac.uk from the directory /pub/joy/. The address for the JOY server is http://www-cryst.bioc.cam.ac.uk/cgi-bin/joy.cgi. CONTACT [email protected]
Nature Genetics | 2014
So-Youn Shin; Eric Fauman; Ann-Kristin Petersen; Jan Krumsiek; Rita Santos; Jie Huang; Matthias Arnold; Idil Erte; Vincenzo Forgetta; Tsun-Po Yang; Klaudia Walter; Cristina Menni; Lu Chen; Louella Vasquez; Ana M. Valdes; Craig L. Hyde; Vicky Wang; Daniel Ziemek; Phoebe M. Roberts; Li Xi; Elin Grundberg; Melanie Waldenberger; J. Brent Richards; Robert P. Mohney; Michael V. Milburn; Sally John; Jeff Trimmer; Fabian J. Theis; John P. Overington; Karsten Suhre
Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
Nature Chemical Biology | 2015
C.H. Arrowsmith; James E. Audia; Christopher M. Austin; Jonathan B. Baell; Jonathan Bennett; Julian Blagg; C. Bountra; Paul E. Brennan; Peter J. Brown; Mark Edward Bunnage; Carolyn Buser-Doepner; Robert M. Campbell; Adrian Carter; Philip Cohen; Robert A. Copeland; Ben Cravatt; Jayme L. Dahlin; Dashyant Dhanak; A. Edwards; Mathias Frederiksen; Stephen V. Frye; Nathanael S. Gray; Charles E. Grimshaw; David Hepworth; Trevor Howe; Kilian Huber; Jian Jin; Stefan Knapp; Joanne Kotz; Ryan G. Kruger
Chemical probes are powerful reagents with increasing impacts on biomedical research. However, probes of poor quality or that are used incorrectly generate misleading results. To help address these shortcomings, we will create a community-driven wiki resource to improve quality and convey current best practice.
Nature Reviews Drug Discovery | 2008
Fernán Agüero; Bissan Al-Lazikani; Martin Aslett; Matthew Berriman; Frederick S. Buckner; Robert K. Campbell; Santiago J. Carmona; Ian M. Carruthers; A.W. Edith Chan; Feng Chen; Gregory J. Crowther; Maria A. Doyle; Christiane Hertz-Fowler; Andrew L. Hopkins; Gregg McAllister; Solomon Nwaka; John P. Overington; Arnab Pain; Gaia V. Paolini; Ursula Pieper; Stuart A. Ralph; Aaron Riechers; David S. Roos; Andrej Sali; Dhanasekaran Shanmugam; Takashi Suzuki; Wesley C. Van Voorhis; Christophe L. M. J. Verlinde
The increasing availability of genomic data for pathogens that cause tropical diseases has created new opportunities for drug discovery and development. However, if the potential of such data is to be fully exploited, the data must be effectively integrated and be easy to interrogate. Here, we discuss the development of the TDR Targets database (http://tdrtargets.org), which encompasses extensive genetic, biochemical and pharmacological data related to tropical disease pathogens, as well as computationally predicted druggability for potential targets and compound desirability information. By allowing the integration and weighting of this information, this database aims to facilitate the identification and prioritization of candidate drug targets for pathogens.
Nature Reviews Drug Discovery | 2017
Rita Santos; Oleg Ursu; Anna Gaulton; Bento Ap; Donadi Rs; Cristian G. Bologa; Anna Karlsson; Bissan Al-Lazikani; Anne Hersey; Tudor I. Oprea; John P. Overington
The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.
Nature Methods | 2011
Bruno Aranda; Hagen Blankenburg; Samuel Kerrien; Fiona S. L. Brinkman; Arnaud Ceol; Emilie Chautard; Jose M. Dana; Javier De Las Rivas; Marine Dumousseau; Eugenia Galeota; Anna Gaulton; Johannes Goll; Robert E. W. Hancock; Ruth Isserlin; Rafael C. Jimenez; Jules Kerssemakers; Jyoti Khadake; David J. Lynn; Magali Michaut; Gavin O'Kelly; Keiichiro Ono; Sandra Orchard; Carlos Tejero Prieto; Sabry Razick; Olga Rigina; Lukasz Salwinski; Milan Simonovic; Sameer Velankar; Andrew Winter; Guanming Wu
To study proteins in the context of a cellular system, it is essential that the molecules with which a protein interacts are identified and the functional consequence of each interaction is understood. A plethora of resources now exist to capture molecular interaction data from the many laboratories generating…