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Modelling the rice proteome Ram Samudrala University of Washington

Modelling the rice proteome Ram Samudrala University of Washington. What is a “proteome”?. For any protein, we wish to:. {. ANNOTATION. }. EXPRESSION + INTERACTION. All proteins of a particular system (organelle, cell, organism). What does it mean to “model a proteome”?.

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Modelling the rice proteome Ram Samudrala University of Washington

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  1. Modelling the rice proteome Ram Samudrala University of Washington

  2. What is a “proteome”? For any protein, we wish to: { ANNOTATION } EXPRESSION + INTERACTION All proteins of a particular system (organelle, cell, organism) What does it mean to “model a proteome”? • 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

  3. Why should we model proteomes? ? Pragmatic reasons: - rational drug design and treatment of disease - protein and genetic engineering - build networks to model cellular pathways - study organismal function and evolution Intellectual challenge: Because it’s there!

  4. De novo prediction of protein structure select sample conformational space such that native-like conformations are found hard to design functions that are not fooled by non-native conformations (“decoys”) astronomically large number of conformations 5 states/100 residues = 5100 = 1070

  5. Semi-exhaustive segment-based folding fragments from database 14-state f,y model generate … … monte carlo with simulated annealing conformational space annealing, GA minimise … … all-atom pairwise interactions, bad contacts compactness, secondary structure filter EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK

  6. CASP5 prediction for T129 5.8 Å Cα RMSD for 68 residues

  7. CASP5 prediction for T138 4.6 Å Cα RMSD for 84 residues

  8. CASP5 prediction for T146 5.6 Å Cα RMSD for 67 residues

  9. CASP5 prediction for T170 4.8 Å Cα RMSD for all 69 residues

  10. CASP5 prediction for T172 5.9 Å Cα RMSD for 74 residues

  11. CASP5 prediction for T187 5.1 Å Cα RMSD for 66 residues

  12. 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

  13. CASP5 prediction for T129 1.0 Å Cα RMSD for 133 residues (57% id)

  14. CASP5 prediction for T182 1.0 Å Cα RMSD for 249 residues (41% id)

  15. CASP5 prediction for T150 2.7 Å Cα RMSD for 99 residues (32% id)

  16. CASP5 prediction for T185 6.0 Å Cα RMSD for 428 residues (24% id)

  17. CASP5 prediction for T160 2.5 Å Cα RMSD for 125 residues (22% id)

  18. CASP5 prediction for T133 6.0 Å Cα RMSD for 260 residues (14% id)

  19. Computational aspects of structural genomics A. sequence space B. comparative modelling C. fold recognition * * * * E. target selection F. analysis D. ab initio prediction * * * * * * * * * * * * * * * * * * * * targets (Figure idea by Steve Brenner.)

  20. Computational aspects of functional genomics structure based methods microenvironment analysis structure comparison sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses zinc binding site? homology function? + assign function to entire protein space * G. assign function * * * * * + experimental data single molecule + genomic/proteomic

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

  22. Bioverse – explore relationships among molecules and systems Jason Mcdermott

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

  24. Bioverse – mapping pathways on the rice predicted network Defense-related proteins Jason McDermott

  25. Bioverse – mapping pathways on the rice predicted network Tryptophan biosynthesis Jason McDermott

  26. Bioverse – network-based annotation Jason McDermott

  27. Bioverse – interactive network viewer Jason McDermott

  28. Take home message Acknowledgements Aaron Chang Ashley Lam Ekachai Jenwitheesuk Gong Cheng Jason McDermott Kai Wang Ling-Hong Hung Lynne Townsend Marissa LaMadrid Mike Inouye Stewart Moughon Shing-Chung Ngan Yi-Ling Cheng Zach Frazier Prediction of protein structure and function can be used to model whole genomes to understand organismal function and evolution

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