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Correlated Mutations and Co-evolution

Correlated Mutations and Co-evolution. May 1 st , 2002. What is Co-evolution (Correlated Mutation)?. Individual regions of proteins interact Regions can be either on the same chain or on different chains (complexes)

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Correlated Mutations and Co-evolution

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  1. Correlated Mutations and Co-evolution May 1st, 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” • Pic percentage of correlated pairs with that distance, Pia 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) • LI and LD 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

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