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Modelling proteomes: Application to understanding HIV disease progression Ram Samudrala
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Modelling proteomes: Application to understanding HIV disease progression Ram Samudrala

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  1. Modelling proteomes: Application to understanding HIV disease progression Ram Samudrala Department of Microbiology 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 ~4500 in bacteria like Salmonella and E. coli 15 in HIV 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 T0215 5.0 Å Cα RMSD for all 53 residues Ling-Hong Hung/Shing-Chung Ngan

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

  9. CASP6 prediction (model1) for T0231 1.3 Å Cα RMSD for all 137 residues (80% ID) Tianyun Liu

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

  11. Similar global sequence or structure does not imply similar function TIM barrel proteins 2246 with known structure hydrolase ligase lyase oxidoreductase transferase

  12. Function prediction from structure Kai Wang

  13. Prediction of protein interaction networks Target proteome Interacting protein database 85% protein a protein A experimentally determined interaction predicted interaction protein B protein b 90% Assign confidence based on similarity and strength of interaction Key paradigm is the use of homology to transfer information across organisms; not limited to yeast, fly, and worm Consensus of interactions helps with confidence assignments Jason McDermott

  14. E. coli predicted protein interaction network Jason McDermott

  15. H. sapiens predicted protein interaction network Jason McDermott

  16. Identification of virulence factors Jason McDermott

  17. Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier

  18. Prediction of HIV-1 protease-inhibitor binding energies with MD Jenwitheesuk E, Samudrala R. Antiviral Therapy 10: 157-166, 2005. Jenwitheesuk E, Samudrala R. BMC Structural Biology 3: 2, 2003. Ekachai Jenwitheesuk

  19. Prediction of HIV-1 protease IC50 values with linear regression Experiment Prediction Wang K, Samudrala R, Mittler J. Journal of Infectious Diseases 190: 2055-2056, 2004. Wang K, Samudrala R, Mittler J. Antiviral Therapy 9: 703-712, 2004. Wang K, Jenwitheesuk E, Samudrala R, Mittler J. Antiviral Therapy 9: 343-352, 2004. Wang K, Samudrala R, Mittler J. Journal of Clinical Microbiology 42: 2353-2354, 2004. Kai Wang, John Mittler

  20. Prediction of inhibitor resistance/susceptibility http://protinfo.compbio.washington.edu/pirspred/ Ekachai Jenwitheesuk/ Kai Wang/John Mittler Jenwitheesuk E, Wang K, Mittler J, Samudrala R. AIDS 18: 1858-1859, 2004. Jenwitheesuk E, Wang K, Mittler J, Samudrala R. Trends in Microbiology 13: 150-151, 2005.

  21. Prediction of fitness in absence of drug John Mittler Mittler J, Samudrala R. submitted, 2005.

  22. Prediction of escape mutants (HIV evolution) Is amino acid change more than one nucleotide mutation away from query sequence? Yes Exclude No Is this amino acid ever observed as a natural polymorphism in untreated patients? Yes Include No Is this amino acid in a database of mutants found to have nonzero fitness in in vitro studies such as those of Loeb et al.? Yes Include No Yes Is the new protein predicted to be nonfunctional based on all-atom score Exclude No Yes Does amino acid change have a high Blosum62 or PAM score? Include No Exclude John Mittler

  23. Acknowledgements Aaron Chang David Nickle Ekachai Jenwitheesuk Gong Cheng Jason McDermott Jeremy Horst 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 John Mittler and Jim Mullins http://protinfo.compbio.washington.edu http://bioverse.compbio.washington.edu