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Modelling proteomes Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

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Presentation on theme: "Modelling proteomes Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?"— Presentation transcript:

1 Modelling proteomes Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

2 Proteome – all proteins of a particular system

3 Modelling proteomes – understand the structure of individual proteins

4 Modelling proteomes – understand their individual functions

5 Modelling proteomes – understand their expression

6 Modelling proteomes – understand their interactions

7 De novo prediction of protein structure sample conformational space such that native-like conformations are found astronomically large number of conformations 5 states/100 residues = 5 100 = 10 70 select hard to design functions that are not fooled by non-native conformations (“decoys”)

8 Semi-exhaustive segment-based folding EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK generate continuous ,  distributions local and global moves …… minimise monte carlo with simulated annealing conformational space annealing, GA …… filter all-atom pairwise interactions, bad contacts compactness, secondary structure, density of generated conformations

9 2.52 Å5.06 Å Model 1 CASP6 prediction for T0215 Ling-Hong Hung/Shing-Chung Ngan

10 3.63 Å 5.42 Å Model 5 CASP6 prediction for T0236 Ling-Hong Hung/Shing-Chung Ngan

11 2.25 Å4.31 Å Model 1 CASP6 prediction for T0281 Ling-Hong Hung/Shing-Chung Ngan

12 Comparative modelling of protein structure KDHPFGFAVPTKNPDGTMNLMNWECAIP KDPPAGIGAPQDN----QNIMLWNAVIP ** * * * * * * * ** …… scan align refine physical functions build initial model minimum perturbation construct non-conserved side chains and main chains graph theory, semfold de novo simulation

13 T0247RAPDFTMscoreRMSDMaxSub cf-model-30.140.84484.0550.6563 parent 1-27.090.83914.1080.6446 parent 2-26.680.83184.1940.625 parent 3-26.590.82524.1970.6051 parent 4-26.250.8393.9810.6281 parent 5-18.510.84223.9790.6416 CASP6 prediction for T0247 Model 1 Tianyun Liu

14 Model 1 Parent 1 Parent 2 Parent 3 T0247RAPDFTM-scoreRMSDMaxSub cf-model-37.440.87182.1660.7911 parent 1-34.870.86622.2330.7789 parent 2-33.990.82482.1660.7402 parent 3-36.830.82542.1390.7456 CASP6 prediction for T0271 Tianyun Liu

15 CASP6 overall summaries Tianyun Liu

16 Similar global sequence or structure does not imply similar function

17 Qualitative function classification Kai Wang

18 Prediction of HIV-1 protease-inhibitor binding energies with MD MD simulation time Correlation coefficient ps 0 0.2 0.4 0.6 0.8 1.0 1.0 0.5 with MD without MD Ekachai Jenwitheesuk

19 Prediction of inhibitor resistance/susceptibility Kai Wang / Ekachai Jenwitheesuk http://protinfo.compbio.washington.edu/pirspred/

20 Integrated structural and functional annotation of proteomes structure based methods microenvironment analysis zinc binding site? structure comparison homology function? sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses + * * * * Bioverse * * assign function to entire protein space experimental data single molecule + genomic/proteomic + EXPRESSION + INTERACTION }

21 Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

22 Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

23 Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

24 Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu

25 Bioverse – prediction of protein interaction networks Jason McDermott Interacting protein database protein α protein β experimentally determined interaction Target proteome protein A 85% predicted interaction protein B 90% Assign confidence based on similarity and strength of interaction

26 Bioverse – E. coli predicted protein interaction network Jason McDermott

27 Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott

28 Bioverse – C. elegans predicted protein interaction network Jason McDermott

29 Bioverse – H. sapiens predicted protein interaction network Jason McDermott

30 Bioverse – network-based annotation for C. elegans Jason McDermott

31 Articulation point proteins Bioverse – identifying key proteins on the anthrax predicted network

32 Jason McDermott Bioverse – identification of virulence factors

33 Bioverse - Integrator Aaron Chang

34 Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution

35 Acknowledgements Aaron Chang Chuck Mader David Nickle Ekachai Jenwitheesuk Gong Cheng Jason McDermott Kai Wang Ling-Hong Hung Mike Inouye Michal Guerquin Stewart Moughon Shing-Chung Ngan Tianyun Liu Zach Frazier National Institutes of Health National Science Foundation Searle Scholars Program (Kinship Foundation) UW Advanced Technology Initiative in Infectious Diseases http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu


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