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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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Proteome – all proteins of a particular system
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Modelling proteomes – understand the structure of individual proteins
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Modelling proteomes – understand their individual functions
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Modelling proteomes – understand their expression
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Modelling proteomes – understand their interactions
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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”)
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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
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2.52 Å5.06 Å Model 1 CASP6 prediction for T0215 Ling-Hong Hung/Shing-Chung Ngan
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3.63 Å 5.42 Å Model 5 CASP6 prediction for T0236 Ling-Hong Hung/Shing-Chung Ngan
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2.25 Å4.31 Å Model 1 CASP6 prediction for T0281 Ling-Hong Hung/Shing-Chung Ngan
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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
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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
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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
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CASP6 overall summaries Tianyun Liu
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Similar global sequence or structure does not imply similar function
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Qualitative function classification Kai Wang
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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
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Prediction of inhibitor resistance/susceptibility Kai Wang / Ekachai Jenwitheesuk http://protinfo.compbio.washington.edu/pirspred/
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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 }
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Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu
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Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier http://bioverse.compbio.washington.edu
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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
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Bioverse – E. coli predicted protein interaction network Jason McDermott
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Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott
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Bioverse – C. elegans predicted protein interaction network Jason McDermott
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Bioverse – H. sapiens predicted protein interaction network Jason McDermott
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Bioverse – network-based annotation for C. elegans Jason McDermott
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Articulation point proteins Bioverse – identifying key proteins on the anthrax predicted network
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Jason McDermott Bioverse – identification of virulence factors
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Bioverse - Integrator Aaron Chang
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Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution
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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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