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Correlated Mutations and Co-evolution

Co-evolution refers to the phenomenon where mutations in one region of a protein influence changes in another, either on the same protein or in associated complexes. This occurs through correlated mutations that provide insights into protein-protein interactions and assist in docking predictions. Methodologies such as the harmonic average and correlation coefficients quantify these relationships, while studies on proteins like myoglobin and chemokines highlight the importance of charge and size in evolutionary dynamics. This research aids in understanding complex biological systems and interactions that underpin immunological responses.

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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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