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

Proteome – all proteins of a particular system

Modelling proteomes – understand the structure of individual proteins

Modelling proteomes – understand their individual functions

Modelling proteomes – understand their expression

Modelling proteomes – understand their interactions

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 = = select hard to design functions that are not fooled by non-native conformations (“decoys”)

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

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

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

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

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

T0247RAPDFTMscoreRMSDMaxSub cf-model parent parent parent parent parent CASP6 prediction for T0247 Model 1 Tianyun Liu

Model 1 Parent 1 Parent 2 Parent 3 T0247RAPDFTM-scoreRMSDMaxSub cf-model parent parent parent CASP6 prediction for T0271 Tianyun Liu

CASP6 overall summaries Tianyun Liu

Similar global sequence or structure does not imply similar function

Qualitative function classification Kai Wang

Prediction of HIV-1 protease-inhibitor binding energies with MD MD simulation time Correlation coefficient ps with MD without MD Ekachai Jenwitheesuk

Prediction of inhibitor resistance/susceptibility Kai Wang / Ekachai Jenwitheesuk

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 }

Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier

Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier

Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier

Bioverse – explore relationships among molecules and systems Jason McDermott/Michal Guerquin/Zach Frazier

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

Bioverse – E. coli predicted protein interaction network Jason McDermott

Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott

Bioverse – C. elegans predicted protein interaction network Jason McDermott

Bioverse – H. sapiens predicted protein interaction network Jason McDermott

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

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

Jason McDermott Bioverse – identification of virulence factors

Bioverse - Integrator 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 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