ayout 1 - EMBL Grenoble

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
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