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Modelling proteomes Ram Samudrala University of Washington

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. ~60,000 in human. ~60,000 in rice. ~4500 in bacteria like Salmonella and E. coli.

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Modelling proteomes Ram Samudrala University of Washington

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

  2. 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

  3. Modelling proteomes – understand the structure of individual proteins A few thousand distinct structural folds

  4. Modelling proteomes – understand their individual functions Thousands of possible functions

  5. Modelling proteomes – understand their expression Different expression patterns based on time and location

  6. Modelling proteomes – understand their interactions Interactions and expression patterns are interdependent with structure and function

  7. CASP6 prediction (model1) for T0281 4.3 Å Cα RMSD for all 70 residues Ling-Hong Hung/Shing-Chung Ngan

  8. CASP6 prediction (model1) for T0271 2.4 Å Cα RMSD for all 142 residues (46% ID) Tianyun Liu

  9. Function prediction from structure http://protinfo.compbio.washington.edu Kai Wang

  10. E. coli predicted protein interaction network Jason McDermott

  11. M. tuberculosis predicted protein interaction network Jason McDermott

  12. C. elegans predicted protein interaction network Jason McDermott

  13. H. sapiens predicted protein interaction network Jason McDermott

  14. Bioverse – v2.0 http://bioverse.compbio.washington.edu Michal Guerquin/Zach Frazier

  15. Bioverse - Integrator Aaron Chang

  16. 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

  17. Acknowledgements Current group members: Past group members: • Andrew Nichols • Baishali Chanda • Chuck Mader • David Nickle • Duangdao Wichadakul • Duncan Milburn • Ersin Emre Oren • Ekachai Jenwitheesuk • Gong Cheng • Jason McDermott • Jeremy Horst • Kai Wang • Ling-Hong Hung • Michal Guerquin • Sarunya Suebtragoon • Shing-Chung Ngan • Stewart Moughon • Tianyun Liu • Weerayuth Kittichotirat • Zach Frazier • Lisa Anichini, Program Manager • Aaron Chang • Marissa LaMadrid • Mike Inouye Funding agencies: • National Institutes of Health • National Science Foundation • Searle Scholars Program • Puget Sound Partners in Global Health • UW Advanced Technology Initiative http://protinfo.compbio.washington.edu http://bioverse.compbio.washington.edu

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