1. Correlated Mutations and Co-evolution May 1 st, 2002

2. What is Co-evolution (Correlated Mutation)? Individual regions of proteins interact Regions can be either on the same chain or on different chains (complexes) A mutation in one half of the pair induces a change in the other half of the pair “the tendency of positions in proteins to mutate co-ordinately” Pazos et. al. 1997

3. “Correlated Mutations Contain Information about Protein-protein interactions” Pazos et. al. 1997 A possible aid to the “docking” problem, using only sequence information Docking: The process by which protein domains interact with one another  fitting

4. Methodology The correlation coefficient S is the similarity between residues at the positions i/j of type k versus l Arbitrarily chosen cutoff M predicted contacts (greatest L/2 values) i.e. M=L/2

5. The Harmonic Average (Xd) Measure of “correlatedness” P ic percentage of correlated pairs with that distance, P ia for all pairs

6. Comparisons of Correlations

7. Docking solutions test Note: larger percentages imply worse performance Special mention of 2gcr and 3adk “sequence information does not seem to be sufficient to discriminate”

8. Figure 5: Scatter plot of Xd vs RMS distance 9pap Hemoglobin 1hbb

9. Prediction: Hsc70 Figure 6: predicted contacts of Nt and Ct domains of Hsc70 Could be verified experimentally

10. Coevolving Protein Residues: Maximum Likelihood and Relationship to Structure. Pollock et. al 1999 Using size and charge characteristics to define co-evolution (correlation) Negative Correlation: Correlation due to differences in charge (and thus also coevolution)

11. The Markov process model (simulated evolution) Two states, A and a Equation 1, the probability of transitioning state λ rate parameter π equilibrium frequency

12. Use of parameters in model Basic model for how they simulate evolutionary steps

13. Likelihood Test Characteristic (LR) L I and L D maximum likelihood values for independent and dependent model Method of determining whether dependence is statistically significant

14. Test of Significance (LR values for change in parameters)

15. Myoglobin Used structure of myoglobin; compared differences in sequences Variety of species used for sequence information; sperm whale 3D protein structure

16. LR distributions for myoglobin: size and charge Note the large negative correlation LR values in charge

17. Co-evolution of Proteins with their Interaction Partners, Goh et. al. 2000 Applied to PGK Chemokines

18. What is PGK?

19. Methodology Two independent sequence alignments, for N and C regions, using PSI-BLAST ClustalW to create distance matrix between complete domains To determine correlation, used equation below X and Y correspond to domains; r a measure of relatedness between these domains

20. PGK correlations

21. Chemokines Role of chemokines; importance in immunity (HIV, cancer) Four categories, mean nothing to me

22. Clustering of Chemokines

23. Clustering of Chemokine receptors