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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Is Over-parameterization a Problem for Profile Mixture Models?

Hector Baños1,2,3, Edward Susko2,3, Andrew J Roger1,3

  • 1Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.

Systematic Biology
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

Profile mixture models effectively estimate protein evolution, even with many parameters. Over-parameterization is not an issue for these models, even in short alignments, improving phylogenetic accuracy.

Keywords:
Frequency profile mixtureslong.branch attractionmixture modelphylogenetics

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Protein sequence evolution exhibits site-specific heterogeneity due to biochemical constraints.
  • Phylogenetic models ignoring site heterogeneity risk long-branch attraction (LBA) artifacts.
  • Profile mixture models address heterogeneity using site classes with distinct amino acid frequencies.

Purpose of the Study:

  • To investigate if over-parameterization in profile mixture models affects phylogenetic tree topology estimates.
  • To assess the performance of profile mixture models with varying numbers of site classes and alignment lengths.
  • To evaluate the impact of misspecified parameters on model accuracy and LBA artifacts.

Main Methods:

  • Theoretical analysis of parameter convergence for long sequences.
  • Simulation studies using short alignments with LBA-prone topologies.
  • Exploration of profile mixture models with and without an overall amino acid frequency class (F-class).

Main Results:

  • Over-parameterization does not hinder estimation for profile mixture models, even with numerous components, especially for longer sequences.
  • Complex profile mixture models with many site classes perform better than simpler models, even on short, LBA-prone alignments.
  • Misspecification of amino acid frequency vectors has minimal impact if the cumulative distribution is accurate, but misspecified exchangeability rates severely affect estimation.
  • An additional F-class generally does not improve parameter estimation and can sometimes decrease tree estimation accuracy despite improving likelihood scores.

Conclusions:

  • Profile mixture models are robust to over-parameterization in phylogenetic inference of protein evolution.
  • Model complexity and accurate estimation of amino acid frequencies and exchangeability rates are crucial for reliable phylogenetic results.
  • The inclusion of an overall amino acid frequency class (F-class) is not consistently beneficial and may even be detrimental to tree topology estimation.