Correlated Mutations and Co-evolution
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Transcript of Correlated Mutations and Co-evolution
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Correlated Mutations and Co-evolution
May 1st, 2002
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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
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“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
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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
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The Harmonic Average (Xd)
• Measure of “correlatedness”
• Pic percentage of correlated pairs with that distance, Pia for all pairs
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Comparisons of Correlations
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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”
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Figure 5: Scatter plot of Xd vs RMS distance
9pap
Hemoglobin 1hbb
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Prediction: Hsc70
• Figure 6: predicted contacts of Nt and Ct domains of Hsc70
• Could be verified experimentally
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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)
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The Markov process model (simulated evolution)
• Two states, A and a• Equation 1, the probability of transitioning state• λ rate parameter• π equilibrium frequency
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Use of parameters in model
• Basic model for how they simulate evolutionary steps
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Likelihood Test Characteristic (LR)
• LI and LD maximum likelihood values for independent and dependent model
• Method of determining whether dependence is statistically significant
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Test of Significance (LR values for change in parameters)
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Myoglobin
• Used structure of myoglobin; compared differences in sequences
• Variety of species used for sequence information; sperm whale 3D protein structure
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LR distributions for myoglobin: size and charge
• Note the large negative correlation LR values in charge
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Co-evolution of Proteins with their Interaction Partners, Goh
et. al. 2000
• Applied to PGK
• Chemokines
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What is PGK?
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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
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PGK correlations
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Chemokines
• Role of chemokines; importance in immunity (HIV, cancer)
• Four categories, mean nothing to me
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Clustering of Chemokines
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Clustering of Chemokine receptors