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Deriving Principles of Protein Structure

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Presentation on theme: "Deriving Principles of Protein Structure"— Presentation transcript:

1 Deriving Principles of Protein Structure
Eric Blanc Nicola Kerrison Eugene Schuster The Molecular Basis of Life Understanding Biology, Disease & Evolution through Protein Structure Jonathan Ward Jonathan Barker Mike Stevens Enzymes & Pathways Deriving Principles of Protein Structure Functional Annotation of genomes, proteins and structures Functional Genomics Analysis of Ageing Pilar Ortega Shiri Freilich Matthew Bashton Tim Massingham Roman Laskowski Rafi Najmanovich James Torrance Victor Chiskoff Fabian Glaser Alex Gutteridge Thornton Group James Watson Gaby Reeves

2 Enzyme Structure, Function and Evolution
Alex Gutteridge Malcolm MacArthur James Torrance Ruth Spriggs Richard George Irilenia Nobeli Shiri Freilich Gareth Stockwell Outstanding Questions: How is catalysis performed - Principles of catalysis? How do enzymes evolve? What is the enzyme complement in different organisms? Can we predict enzyme function from structure? Can we design new enzymes?

3 Percentages of entries in Data Resources annotated as enzymes
No. enzymes No. in database %-tage PDB (all entries) , , PDB (non-redundant) , , UniProt , , Reactome (human) ( ) Trivial search " (via UniProt) Roman Laskowski

4 Ligand Selectivity Conformational Change Templates Metabolome Catalytic Site Atlas Binding Site Diversity Spherical Harmonics

5 Enzymes Catalytic Residues – Catalytic Site Atlas
Mechanisms of catalysis Annotation using CSA Nature’s Catalytic Toolkit The enzyme complement in various organisms

6 The Class A β-Lactamase Mechanism

7 -lactamase Class A; EC 3.5.2.6; PDB: 1btl
The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Craig T. Porter, Gail J. Bartlett, and Janet M. Thornton Nucl. Acids. Res : D129-D133. -lactamase Class A; EC ; PDB: 1btl Reaction: -lactam + H2O  -amino acid Active site residues: S70, K73, S130, E16

8 Propensity of residues to have a catalytic function
Alex Gutteridge

9 CSA Coverage (Current 514 Enzyme Dataset) Craig Porter & Gail Bartlett
# Representative Sites = 512 # PDB Files = 9,075 # Catalytic Sites = 20,001 Class # CSA/ # PDB E.C Oxidoreductases / 271 E.C Transferases / 280 E.C Hydrolases / 421 E.C Lyases / 122 E.C Isomerases / 63 E.C Ligases / 58 Total / 1215 Craig Porter & Gail Bartlett

10 Comparison of CSA, SwissProt & PDB
Figure 2. A comparison of residue annotation between the CSA and SwissProt (left) and PDB (right) for the ‘original’ enzyme dataset, in Venn diagram form. The number of residues annotated by each database is given, along with the number of identical residues found in both databases. Craig Porter & Gail Bartlett

11 iCSA: using Residue Conservation to Improve Function Annotation
Starting with over 500 enzymes from the CSA, with EC numbers and high quality catalytic site information Retrieve homologues from BiopendiumTM Align homologues with query enzyme, using PSI-BLAST profiles CLUSTAL W multiple alignments Smith and Waterman pairwise alignments Check for conservation of catalytic residues If all residues are conserved, assign EC from annotated enzyme to homologue Richard George & Ruth Spriggs

12 EC Assignment Accuracy
Correct EC assigned An EC assigned iCSA Richard George & Ruth Spriggs

13 iCSA vs. Sequence Homology Alone
The accuracy of EC assignment is improved by using iCSA The improvement in accuracy is more pronounced with more distant homologues: from 7% at iteration 1 to 88% at iteration 4 Overall, EC assignment accuracy is improved by 48% Overall, EC assignment accuracy using iCSA is 86% (vs. 58% using sequence homology alone) Richard George & Ruth Spriggs

14 MACiE

15 Catalytic Units – Nature’s catalytic Toolkit
Favourable interactions provide enhanced catalytic power. We expect common units to have evolved many times. Alex Gutteridge

16 Catalytic Units The triad comprises two different units:
Carboxylate-Histidine (forms base) Histidine-Hydroxyl (forms nucleophile) Both units can be used independently of the other… …and have evolved separately many times Alex Gutteridge

17 Interaction Dataset 191 structures:
non-redundant annotated by CSA < 2A resolution Occupancy = 1 Polar only Interaction where atom-atom <4A. Alex Gutteridge

18 This figure shows the number of interactions where only one of the residues has to be annotated as catalytic. Again, few polar-polar interactions are observed. More interactions between charged residues and charged residues with polar. We see more carboxylate-carboxylate interactions than we would expect - useful for raising pKa. Alex Gutteridge

19 Common Interactions Some are seen more often than expected…
Alex Gutteridge

20 Common Interactions Some are seen more often than expected…
…some less often Alex Gutteridge

21 Complement of enzymes in 3 domains of life
Examine Number of enzymes in different species as % of the proteome Reaction composition in different species Previous Work: Van Nimwegen et al, 2003: metabolic genes occupy a roughly constant,though not necessarily common, fraction of the genome within the three domains of life Cases et al, 2003: Proteins involved in “small molecule metabolism” are clearly over-represented in small genomes Shiri Freilich

22 Relating the number of enzymes to proteome size
Permissive set KEGG assignments Conservative set human mouse worm fly Shiri Freilich, Ruth Spriggs & Richard George

23 Future Catalytic Mechanisms – Macie Database
with Mitchell, Goodman, Glen & Murray-Rust in Chemistry Informatics Centre at Cambridge Evolution of enzymes, pathways & metabolism in different organisms & tissues Substrate recognition from structure Design??

24 Functional Genomic Analysis of Ageing
Eric Blanc Eugene Schuster Nicola Kerrison Jonathan Ward Good afternoon ladies (are there any?) and gentlemen. Our proposal is for functional genomic analysis of ageing. There are 6 applicants, and we are all here.

25 Bioinformatics The Collaboration UCL & EBI
Oxidative damage and metabolism Brand Gems Ageing Worms IIS/CR Ageing Mouse IIS/CR Humans Leevers Ageing Flies Partridge Withers Thornton Bioinformatics

26 Comparative Functional Genomics
Caenorhabditis elegans Drosophila melanogaster Mus musculus Our approach to discovering and understanding mechanisms of ageing is comparative functional genomics. We consider this to be a powerful generic approach to analysing complex biological traits such as ageing. *We start with two invertebrate model organisms, the nematode worm C. elegans and the fruit fly Drosophila, both of which have completed genome sequences, which are essential for this approach to work. We find and analyse mechanisms of ageing that these two evolutionarily very diverged organisms have in common, because this provides us with candidate mechanisms that may regulate ageing in mammals. To do this we take a two-pronged approach. First, we shall identify and study single gene mutations that slow the ageing process. There are already about 100 such mutations in the worm and about 10 in Drosophila that have proved highly informative. We shall identify further such mutations using a bioinformatic approach - comparative genomics and by mutagenesis in the worm. Second, we shall use whole-genome transcript-profiling to identify genes that mediate the effects of environmental interventions that slow ageing, and downstream elements of pathways from mutants. Throughout this process, we are reliant on *bioinformatics to identify homologues in these two organisms, and to get information about new genes from the transcription profiles. Then, for those pathways that are conserved between these two invertebrate organisms, we use bioinformatics to identify homologous genes and pathways in the *mouse, and we then determine if they modulate the rate of ageing. If they pass this test then we again use bioinfnormatics to identify homologues in *humans that could be targets for beneficial interventions. Bioinformatics Whole genomes Mutants Transcriptomes Pathways Evolutionary conservation Genes

27 Population projections for the UK
By 2050 life expectancy at birth will be 87 By 2020 one fifth of the UK population will be over 65 By 2020 the health care demand will double

28 Interventions that slow ageing in diverse organisms
Reduced insulin/IGF-like signalling Caloric restriction Caloric restriction And Reduced insulin/IGF signalling

29 Caloric Restriction yeast rhesus C. elegans rat Daphnia mouse
Drosophila medfly mouse During caloric restriction the animal is put on a diet - to about 50% of voluntary intake. This extends lifespan in organisms ranging from yeast to mammals, probably including primates. It does it in our 3 model organisms. It decreases the incidence of virtually all age-related damage and disease. D. Gems et al. Genetics 1998 T. Chapman & L. Partridge Proc. Roy. Soc. 1996 A. Bartke et al. Nature 2001

30 Downstream Mechanisms of Ageing: The Free Radical Theory
Superoxide activates mitochondrial uncoupling proteins. K.S. Echtay, M.D. Brand et al. Nature 2002 c I IV ADP + Pi NADH 2 Succinate Fumarate III II ATP H + NAD O2 H2O CoQ e- Uncoupling Matrix O2.- We also wish to understand the mechanisms by which these interventions and pathways modulate the impact of ageing-related damage. The leading candidate mechanism here is damage from free radicals, particularly superoxide and its derivatives. The main site of superoxide production in cells is thought to be at the electron transfer chain in the inner membrane of the mitochondrion. Modulations of the impact of oxidative damage could occur either by adjustment of the various form of endogenous defence mechanisms or of the rate at which they are generated in mitochondria. The evidence on this point is somewhat sparse, and comes mainly from comparison of species with different rates of ageing.

31 Programme of Research FlyBase Gene Ontology Affymetrix whole-
genome chips CALORIC RESTRICTION AGE EXPRESSION LEVEL SURVIVAL MORTALITY AGE CLUSTER ANALYSIS A3 R-7 R-14 R-28 C-23 C-28 C-7 C-18 R-64 R-69 R-47 R-55 R-74 C-42 C-47 FlyBase Gene Ontology The first part will be handling data from transcript profiles. We have done this so far in the context of a microarray profile of the effects of chronic caloric restriction in Drosophila, using whole-genome Affymetrix arrays. The survival and mortality profiles for the flies are shown top left, with the times at which we sampled. 5 chips per point, 75 chip experiment, times abut 14,000 genes. We found we had a lot of statistical power - using replication and temporal information - give some details. This allows us to cluster the profiles as shown bottom right - and one of the main findings of the study was that CR slows ageing-related changed in transcript profile - the chips cluster by physiological age rather than treatment. And some of the many patterns of change in expression are shown on the bottom left - with CR slowing them. And we connect out data base to Gene Ontology in Fly Base to get functional and process ontology, to get a handle on what biologicla processes are affected by age and by our intervention - generates hypotheses for studies using RNAi, overexpression, mutants. Within this project, there’ll be a lot more of this - time courses for CR, downstream effects of mutants, biomarkers etc. And we anticipate the development of new methods as the programme proceeds. We shall also have a lot of other types of data -survival, metabolic, Tissue-and stage-specificity of effects and so on.

32 Programme of Research Mechanisms by which Caloric Restriction and reduced Insulin/IGF-like signalling slow ageing: Mutant screens Global genomic transcript profiles of transitions Ligands: detection, deletion, binding proteins Identify responding tissues The receptor & Signalling from the receptor Interaction in determination of mortality The role of reactive oxygen and nitrogen species: Defence: drugs and endogenous: Production of reactive oxygen species Role of insulin/IGF signalling in mammalian ageing: Loss-of-function mutants: So, what are WE going to do? The experimental part of our programme can be divided into 4 part. First, we shall investigate heading investigate mechanisms by which caloric restriction and reduced insulin/IGF-like signalling low ageing in flies and worms. For CR the initial aim is to identify the genes that mediate the response, whereas for IIS we shall characterise the molecular mechanisms underlying this pathway’s effects. And we shall also investigate the extent to which the mechanisms underlying these two interventions overlap.

33 What is ageing ? 1 -1 20 30 40 50 0.9 -1.5 c 0.8 -2 0.7 -2.5 0.6 Survivorship -3 Ln mortality (µx) 0.5 -3.5 0.4 -4 0.3 -4.5 Fully Feed Flies Fully Fed 0.2 -5 0.1 Calorie Restricted Flies Dietary Restriction -5.5 10 20 30 40 50 60 70 80 -6 Age/ days Days Is the increase in lifespan due to a shift or change in slope? A change in slope means that the flies will die at a different rate and a shift in the slop means that the flies will die at a the same rate but will start to die at a later date. Ln mortality = ln (risk of dying)= ln (no. dead/total at time dt) Mair,W et al. Science 2004

34

35 How to extend lifespan? Lifespan can be manipulated, but how does this work? Popular theories: Reduce oxidative damage Reduce production of reactive oxygen species like superoxide. Increase defence against damage by reactive oxygen species. Increase disposal of “waste” – endobiotic and xenobiotic substances (Green theory). Increase DNA repair (mitochondrial DNA, telomeres). Improve immune response.

36 AgeBase: a QC tool for microarray experiments
Web site for storage and processing microarray data Simple repository for data prior to deposition A QC report is generated at every processing step (raw data, normalisation, p-values computation, gene list generation) Check that the processing step has been successful, and that the hypothesis for the next processing step are fulfilled Eric Blanc & Gene Schuster

37 Comparative transcriptomics of longevity
Are there common gene expression patterns in long-lived animals of different species, or between different tissues in a long-lived individual? Liver Muscle Colon Worm Fly Mouse Nicola Kerrison

38 Summary of results Hypothalamus
 Hormone-mediated signalling ↓ Development / morphogenesis / organogenesis Colon Liver  Ion transport  Rhythmic / circadian behaviour  Cell growth / maintenance / proliferation  Ion transport  Rhythmic / circadian behaviour  Mitochondrial function  Fatty acid metabolism ↓ Steroid / sterol / cholesterol biosynthesis ↓ Amino acid/ amine biosynthesis Muscle  Fatty acid metabolism ↓ Intracellular protein transport ↓ Macromolecule / protein metabolism ↓ Collagen ↓ Blood vessel development

39 Thanks to Funding from:
Wellcome Trust BBSRC/DTI MRC Dept of Energy, US NIH; US Inpharmatica EMBL

40 Thornton Group Eric Blanc Nicola Kerrison Jonathan Barker Pilar Ortega
Rafi Najmanovich Fabian Glaser James Watson Jonathan Ward Roman Laskowski Gareth Stockwell Thornton Group Eugene Schuster Thomas Funkhouser Victor Chiskoff Mike Stevens Tim Massingham Alex Gutteridge Malcolm MacArthur Hannes Ponstingl Shiri Freilich Matthew Bashton Irilenia Nobeli James Torrance Ruth Spriggs Richard George


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