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Modelling proteomes Ram Samudrala Department of Microbiology. How does the genome of an organism specify its behaviour and characteristics?. Proteome – all proteins of a particular system. ~60,000 in human. ~60,000 in rice. ~4500 in bacteria like Salmonella and E. coli.
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Modelling proteomes Ram Samudrala Department of Microbiology How does the genome of an organism specify its behaviour and characteristics?
Proteome – all proteins of a particular system ~60,000 in human ~60,000 in rice ~4500 in bacteria like Salmonella and E. coli Several thousand distinct sequence families
Modelling proteomes – understand the structure of individual proteins A few thousand distinct structural folds
Modelling proteomes – understand their individual functions Thousands of possible functions
Modelling proteomes – understand their expression Different expression patterns based on time and location
Modelling proteomes – understand their interactions Interactions and expression patterns are interdependent with structure and function
Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein
Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) unique shape precisely ordered stable/functional globular/compact helices and sheets Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein
Methods for obtaining structure Experimental Theoretical X-ray crystallography NMR spectroscopy De novo prediction Homology modelling
De novo prediction of protein structure sample conformational space such that native-like conformations are found select hard to design functions that are not fooled by non-native conformations (“decoys”) astronomically large number of conformations 5 states/100 residues = 5100 = 1070
Semi-exhaustive segment-based folding Make random moves to optimise what is observed in known structures generate … … Find the most protein-like structures minimise … … all-atom pairwise interactions, bad contacts compactness, secondary structure, consensus of generated conformations filter EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
Critical Assessment of protein Structure Prediction methods (CASP) Pre-CASP CASP Bias towards known structures Blind prediction
CASP6 prediction (model1) for T0215 5.0 Å Cα RMSD for all 53 residues Ling-Hong Hung/Shing-Chung Ngan
CASP6 prediction (model1) for T0281 4.3 Å Cα RMSD for all 70 residues Ling-Hong Hung/Shing-Chung Ngan
Homologous proteins share similar structures Gan et al, Biophysical Journal 83: 2781-2791, 2002
Comparative modelling of protein structure scan align KDHPFGFAVPTKNPDGTMNLMNWECAIP KDPPAGIGAPQDN----QNIMLWNAVIP ** * * * * * * * ** … … build initial model construct non-conserved side chains and main chains minimum perturbation graph theory, semfold refine physical functions de novo simulation
CASP6 prediction (model1) for T0231 1.3 Å Cα RMSD for all 137 residues (80% ID) Tianyun Liu
CASP6 prediction (model1) for T0271 2.4 Å Cα RMSD for all 142 residues (46% ID) Tianyun Liu
Similar global sequence or structure does not imply similar function TIM barrel proteins 2246 with known structure hydrolase ligase lyase oxidoreductase transferase
Qualitative function classification sequence-based structure-based Kai Wang
Prediction of HIV-1 protease-inhibitor binding energies with MD Can predict resistance/susceptibility to six FDA approved inhibitors with 95% accuracy in conjunction with knowledge-based methods http://protinfo.compbio.washington.edu/pirspred/ Ekachai Jenwitheesuk
Prediction of protein interaction networks Target proteome Interacting protein database 85% protein a protein A experimentally determined interaction predicted interaction protein B protein b 90% Assign confidence based on similarity and strength of interaction Key paradigm is the use of homology to transfer information across organisms; not limited to yeast, fly, and worm Consensus of interactions helps with confidence assignments Jason McDermott
E. coli predicted protein interaction network Jason McDermott
M. tuberculosis predicted protein interaction network Jason McDermott
C. elegans predicted protein interaction network Jason McDermott
H. sapiens predicted protein interaction network Jason McDermott
Network-based annotation for C. elegans Jason McDermott
Identifying key proteins on the anthrax predicted network Articulation point proteins Jason McDermott
Identification of virulence factors Jason McDermott
Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier
Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier
Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier
Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier
Bioverse - Integrator Aaron Chang
Where is all this going? + + Computational biology Structural genomics Functional genomics Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution
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