Modelling proteomes Ram Samudrala University of Washington What

Modelling proteomes Ram Samudrala University of Washington

What is a “proteome”? All proteins of a particular system (organelle, cell, organism) What does it mean to “model a proteome”? For any protein, we wish to: ANNOTATION { - figure out what it looks like (structure or form) - understand what it does (function) Repeat for all proteins in a system Understand the relationships between all of them } EXPRESSION + INTERACTION

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

CASP 5 prediction for T 138 4. 6 Å Cα RMSD for 84 residues

CASP 5 prediction for T 146 5. 6 Å Cα RMSD for 67 residues

CASP 5 prediction for T 170 4. 8 Å Cα RMSD for all 69 residues

CASP 5 prediction for T 129 5. 8 Å Cα RMSD for 68 residues

CASP 5 prediction for T 172 5. 9 Å Cα RMSD for 74 residues

CASP 5 prediction for T 187 5. 1 Å Cα RMSD for 66 residues

CASP 5 independent assessor’s results http: //protinfo. compbio. washington. edu

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

CASP 5 prediction for T 129 1. 0 Å Cα RMSD for 133 residues (57% id)

CASP 5 prediction for T 182 1. 0 Å Cα RMSD for 249 residues (41% id)

CASP 5 prediction for T 150 2. 7 Å Cα RMSD for 99 residues (32% id)

CASP 5 prediction for T 185 6. 0 Å Cα RMSD for 428 residues (24% id)

CASP 5 prediction for T 160 2. 5 Å Cα RMSD for 125 residues (22% id)

CASP 5 prediction for T 133 6. 0 Å Cα RMSD for 260 residues (14% id)

Livebench 7 automated assessment for 71 targets http: //protinfo. compbio. washington. edu

Prediction of protein-inhibitor binding energies with dynamics Correlation coefficient HIV protease 1. 0 with MD 0. 5 without MD 0 0. 2 0. 4 0. 6 0. 8 1. 0 ps MD simulation time Ekachai Jenwitheesuk

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

Prediction of SARS Co. V proteinase inhibitors Ekachai Jenwitheesuk

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

Bioverse – explore relationships among molecules and systems http: //bioverse. compbio. washington. edu Jason Mc. Dermott

Bioverse – explore relationships among molecules and systems Jason Mcdermott

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

Bioverse – E. coli predicted protein interaction network Jason Mc. Dermott

Bioverse – M. tuberculosis predicted protein interaction network Jason Mc. Dermott

Bioverse – C. elegans predicted protein interaction network Jason Mc. Dermott

Bioverse – H. sapiens predicted protein interaction network Jason Mc. Dermott

Bioverse – network-based annotation for C. elegans Jason Mc. Dermott

Bioverse – identifying key proteins on the anthrax predicted network Articulation point proteins Jason Mc. Dermott

Bioverse – identification of virulence factors Jason Mc. Dermott

Bioverse – viewer Aaron Chang

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 Mc. Dermott 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