Julius B. Lucks/Meetings and Notes/SMBE2007/models protein evolution 2

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Ziheng Yang : A mutation selection model of codon substitution

Tue Jun 26 14:02:43 EDT 2007

  • first one to propose codon models - analyze codon changes dN/dS (rather than nucleotides and AAs)
  • UCL
  • Rasmus Nielsen - Univ. Copenhagen
  • Alan Moses thinks this will be a revolutionary talk

Words to Look Up

Talk

  • Goldman & Yang 1994 model - codon substitution model
    • Mol. Biol. Evol., 11, 725, 1994
    • Yang and Nielsen, 1998, J. Mol. Evol.
    • Subst. rate to codon j proportional to equil. freq of codon j
    • does not separate mutational bias and selection on codon usage
    • Bierne & Eyre-Walker, 2003, Genetics, 165, 1587
    • Yang 2006, Computational Molecular Evolution, Oxford, 284
    • TTT,TTC,TCT,TCC transitions - only 2 rates
      • TTC, TCC preferred, others rare
      • realistically want 3 rates
  • model with 3 rates - Neilsen et al. - 20007, Mol. Biol. Evol., 24, 228
    • large rate from unpreferred to pref
    • smal rate for reverse
    • middle rate from pref to pref and unpref to unpref
      • requires a prioir partitioning of codons (Hiroshi Akashi)
  • Codon usage gen believed to be under selection in bacteria and Drosophila
    • mammals case is less clear
    • Akashi H, 1994, Genetics
    • synonymous changes change protein structure and function
      • Kimchi-Sarfaty, 2007, Science, 315, 525
      • Komar AA, 2007, Scienc, 315, 466
      • protein folding co-translational
      • silent SNP - altered protein translation kinetics - final protein diff conformation and function

Model

  1. mutation rate from nucl i to j described by HKY85 or GTR (REV) applied to all 3 processes
    • \mu_{ij} = a_{ij}\pi_j^* - a's symmetric
    • π * mutational bias parameters
    • codons I = i1i2i3
  2. fixation probability function of selection coefficient
    • Kimura M, 1962, Genetics, 47, 713 - use Kimura formula
      • Sij = 2Nsij = 2N(fjfi)
      • N number of chromosomes
  3. Selection on protein is modeled using ω
  • parameters in the model
    • 4 mutation rates
    • 60 codon fitness parameters
    • sequence distance or branch lengths
  • time reversible
    • markov change tr iff rate matrix is product of symmetrical matrix and diagonal matrix
    • equil rate of codon \pi_j \propto (\pi_{j1}^*\pi_{j2}^*\pi_{j3}^*)e^{F_J}
  • comments
    • use of omega to detect selection on the protein does not rely on assump that synon sites evolve neutrally
    • old medels in codeml such as F1x4, F3x4, Fcodon - not special cases of mutation selection model
    • Muse and Gaut, 1994 ,mBE , 11, 715

Results

  • why little correlation between ωhumanmacaque and ωmouserat?
  • liklihood ration test of selection on synonymous codon usage
    • null model assumes synonymous codons have same fitness
    • most genes are under selection of codon usage

Summary

  • estimation of distances using old models fine
  • in most (90%) genes - sig evidence for nat selection driving evol of codon usage
  • most mutations have fitness in range |S| < 1 or 2, implying weak selection on codon usage or nearly neutral evolution

Questions

  • drosophila and bacteria - codon bias and gene expression found
    • expts - optimal codons can use in bacteria - use to translate more eff
    • mammals not as clear

Tal Pupko : An evolutionary model that accounts for selection on synonymous mutations

Tue Jun 26 14:03:00 EDT 2007

  • Cell Res Immunology - Tel-Aviv
  • Ka/Ks webserver
  • collaborated with Nir Friedman

Words to Look Up

  • positive selection vs. purifying selection

Talk

  • codon models
    • enference of evel selection forces on a protein
    • purify selection
    • phylogeny
  • converting empirical AA replacement matrices into codon-based subst matrices
  • methods for computing Ka/Ks
    • subst. matrix rates 61x61
  • Yang's M model (2000)
    • K - transition/transversion ratio
    • Π - codon frequency
    • w - factor of selection
  • problems
    • asummes rate of leu (UUG) to tryp (UGG) = rate leu (UUG) to phe (UUU) (single transvertion)
      • 1st 5 times more likely
      • model does not account for exact identity of AA
    • assumes instan rate betwiin two AAs that differ by one mutation ...
  • propose model
    • Mechanistic Empirical Combined (MEC)
    • exapand 20x20 empirical AA matrix into 61x61 codon matrix
  • assumptions
    • sum of rate of all codons = sum of rates of AAs, but take into account codon and AA probabilities
    • intensity of selection - omega - assume gamma distributed

Ks conservation

  • most models assume Ks (synonymous) same for all sites(reflects neutral rate of evolution)
    • is this true?

HIV

  • vif and pol overlap in diff frames - reduced Ks in these regions

Further

  • large scale search for conserved ks in mammals, viruses, bacteria and yeasts
  • impact of Ks conservation on positive selection inference
  • charcterization of conserved Ks regions
  • Goren, Mol. Cell, 2006

Questions

Claudia Kleinman : Protein structure and sequence evolution - statistical potentials for phylogeny

Tue Jun 26 14:03:26 EDT 2007

Words to Look Up

Talk

  • probabilistic models of sequence evolution
  • try to incorporate protein structure explicitly into the models
  • site-dependant approaches
    • simulation of evolution: Parisi & Echave 2001
  • statistical potential
    • knowledge-based energy function derived from analysis of known protein structures
    • Q_{lm}r_le^{\beta(G_l - G_m)}
    • coarse grain structure
    • accounts for implicitly for poorly understood complex effects
  • pairwise potential that depends on distance between residues (w/ solvent accessibility potentials)
  • contact potentials
  • optimized for structure prediction problem

Devise stat potential for an evol context

  • E = Econtact + Esolvent + Etorsion + ESS
    • derive contact map (binary n.n.'s)
      • contact energy parameter
    • solvent accessibility - arb # of classes
    • torsion - use main chain angles
  • Kleinman, BMC, 7, 326, 2006
  • likelihood proportional to exp of negative of this energy (chemical potential)
    • maximize (maximum likelihood)
    • estimate gradient by MCMC - follow to find the maximum

model comparison using Bayes factors

  • Rodrigue, MBE, 23, 1762, 2006
    • Poisson distr ref model
    • thermodynamic integration


Questions

Mario Fares : The three-dimensionality of molecular evolution

Tue Jun 26 14:03:53 EDT 2007

  • Trinity College Dublin

Words to Look Up

Talk

  • detecting selective constraints in protein-coding genes: survival of the fittest
  • ω = dN / dS
    • ω < 1 > - purifying
    • ω > 1 > - positive selection


Questions

Allan Drummond : Modeling evolution when ribosomes fail

Tue Jun 26 14:04:15 EDT 2007

  • w/ Claus Wilke - UT Austin

Words to Look Up

Talk

  • ribosomes fail - don't ignore when model protein evol
  • near-universal observations
    • coding sequences evolve at very diff rates
    • dN and dS correlate
    • high expressed proteins evolve slowly
    • codons matching abundant tRNAs preferred
      • high expressed genes
      • conserved sites (Akashi 1994 - trans accuracy)
    • codon biased genes have fewer dS and dN
  • in matrix form - matrices look like block structure
    • bad news - not independant - PCA would predict just one factor
  • 1 protein in 5 mistranslated
    • can still fold
    • or can misfold
  • selection can act to favor protein sequences that are robust to mistranslation
  • certain codons translated 6 times more accurately (model)
  • lattice protein model
    • Bloom, PNAS (2005,2006)
    • Taverna, Goldstein, Proteins (2001)
  • anything within 5 kCal/mol of gs will fold
  • translational selection alone sufficient to explain the observed correlation matrix patters
  • Akashi 1994
    • select for speed - don't matter where opt codons are - have to go through all of them
    • select for acc - should put opt codons at most highly conserved sites
    • this allows a within gene test to see what matters most

Conclusions

  • evol rate should be considered as regulatory as well as functional signal
  • translational selection suffices to explain many evol patterns
  • brute-force modelling of protein evol possible

Questions

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