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EMBL-EBI, Hinxton, UK The European Bioinformatics Institute (EMBL-EBI) lies in the 55 acres of landscaped parkland in rural Cambridgeshire that make up the Wellcome Trust Genome Campus, which also houses the Wellcome Trust Sanger Institute. Together, these institutes provide one of the world’s largest concentrations of expertise in genomics and bioinformatics. EMBL-EBI has a fourfold mission: • to provide freely available data and bioinformatics services to all facets of the scientific community in ways that promote scientific progress; • to contribute to the advancement of biology through basic investigator-driven research in bioinformatics; • to provide advanced bioinformatics training to scientists at all levels, from PhD students to independent investigators; • to help disseminate cutting-edge technologies to industry. As a hub of bioinformatics in Europe, EMBL-EBI provides data resources in all the major molecular domains and grew out of EMBL’s pioneering work in providing public biological databases to the research community. Its comprehensive range of data resources includes the European Nucleotide Archive (DNA and RNA sequences); Ensembl (genomes); ArrayExpress (transcriptomics and gene expression data); UniProt (protein sequences and functional information); PDBe (protein structures); InterPro (protein families, motifs and domains); IntAct (molecular interactions); and Reactome (pathways). All of these resources are the products of international collaborations with other data providers. As the coordinator of ELIXIR, an EU-funded project to agree upon the future bioinformatics infrastructure for Europe, we are working with scientists and funders throughout Europe to pave the way towards a more stable footing for Europe’s core data resources. We have a broad palette of research interests that complement our data resources and these two strands of activity are mutually supportive, with many collaborations between research groups and service teams. Eight research groups aim to understand biology through the development of new approaches to interpreting biological data. These diverse approaches include classifying and understanding proteins and their interactions; mathematical analyses of evolutionary models; computational modelling of neuronal signalling; text mining; statistical approaches to functional genomics; large-scale analysis of regulatory systems and differentiation and RNA genomics. In addition, our services teams perform extensive research to enhance existing data resources and develop new ones. The EBI also provides user training, both on-site, through its hands-on training programme, and off-site through the Bioinformatics Roadshow. More information is available at www.ebi.ac.uk/training. Almost all of our groups offer PhD places through the EMBL International PhD Programme. EMBL-EBI’s PhD students register with, and obtain their doctorates from, the University of Cambridge. For a list of those groups with PhD places available for 2010/2011, please see the PhD studies section of our training web pages: www.ebi.ac.uk/training/Studentships. Other positions are advertised through the EMBL jobs pages (www.embl.org/jobs). Janet Thornton Director, EMBL-EBI
EMBL Research at a Glance 2009 Computational biology of proteins – structure, function and evolution Janet Thornton PhD 1973, King’s College & National Inst. for Medical Research, London. Postdoctoral research at the University of Oxford, NIMR & Birkbeck College, London. Lecturer, Birkbeck College 1983-1989. Professor of Biomolecular Structure, University College London (UCL) since 1990. Bernal Professor at Birkbeck College, 1996-2002. Director of the Centre for Structural Biology at Birkbeck College and UCL, 1998-2001. Director of EMBL-EBI since 2001. Previous and current research The goal of our research is to understand more about how biology works at the molecular level, how enzymes perform catalysis, how these molecules recognise one another and their cognate ligands, and how proteins and organisms have evolved to create life. We develop and use novel computational methods to analyse the available data, gathering data either from the literature or by mining the data resources, to answer specific questions. Much of our research is collaborative, involving either experimentalists or other computational biologists. During 2008 our major contributions have been in the following five areas: • enzyme structure and function; • using structural data to predict protein function and to annotate genomes; • evolutionary studies of genes, their expression and control; • functional genomics analysis of ageing; • development of tools and web resources. Future projects and goals We will continue our work on understanding more about enzymes and their mechanisms, including a study of how the enzymes, their families and their pathways have evolved. We will develop new computational tools to improve the handling of mechanisms and their reactions, which will allow improved chemistry queries across our databases. We are looking more closely at drug– protein interactions, membrane proteins (in collaboration with Professor David Jones at University College London) and allosteric effects. In the ageing project we are interested in tissue specificity and using human public transcriptome datasets to explore effects related to human variation and age. We have used protein–ligand docking as a tool for protein function identification. The figure shows the physical chemical characterisation of the top ten hits from docking approximately 1,000 human metabolites to six members of the short chain dehydrogenase/reductase family of enzymes. The plots show eight 1D descriptors as colours, where the size of the sector reflects the value of the descriptor. These descriptors are: LogP, # H-bond donors; # H-bond acceptors, Molecular Weight, Charge, ~ rings, # rotatable bonds, ~ aromatic atoms. The first column shows plots for the ‘known’ cognate substrate for comparison. The plot highlights that in the first two rows all ten top hits are similar and resemble the substrate. The middle two examples show hits which are all different to each other and different from the substrate. However these two enzymes are known to be promiscuous. In the bottom two examples, all the hits look alike but are different from the known substrate. This is probably due to the inaccuracies of the scoring functions, and these results improve if the energy is recalculated with more sophisticated energy functions. Selected references Favia, A.D. et al. (2008). Molecular docking for substrate identification: The short-chain dehydrogenases/reductases. J. Mol. Biol., 375, 855-87 Laskowski, R.A. & Thornton, J.M. (2008). Understanding the molecular machinery of genetics through 3D structures. Nat. Rev. Genet., 9, 11-151 Najmanovich, R. et al. (2008). Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites. Bioinformatics, 2, i105-111 Holliday, G.L. et al. (2007). The chemistry of protein catalysis. J. Mol. Biol., 372, 1261-1277 6
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