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Modelling, comparison, and analysis of proteomes Ram Samudrala University of Washington

Modelling, comparison, and analysis of proteomes 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).

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Modelling, comparison, and analysis of proteomes Ram Samudrala University of Washington

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  1. Modelling, comparison, and analysis of proteomes 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. 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 * Bioverse * * * * * + experimental data single molecule + genomic/proteomic

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

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

  7. 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

  8. Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott

  9. Bioverse – E. coli predicted protein interaction network Jason McDermott

  10. Bioverse – V. cholerae predicted protein interaction network Jason McDermott

  11. Bioverse – C. elegans predicted protein interaction network Jason McDermott

  12. Bioverse – H. sapiens predicted protein interaction network Jason McDermott

  13. Bioverse – organisation of the interaction networks Ci = 2n/ki(ki-1) Jason McDermott

  14. Bioverse – viewer Aaron Chang

  15. 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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