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Ambiguity Coding Allows Accurate Inference of Evolutionary Parameters from Alignments in an Aggregated State-Space.

Claudia C Weber1, Umberto Perron1, Dearbhaile Casey1

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Learn protein evolution history by adapting models for missing data. This method recovers evolutionary information from sequences previously inaccessible, improving ancestral reconstruction and selection strength estimates.

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Biophysics

Background:

  • Understanding protein evolution requires models that capture genetic variation and functional constraints.
  • Practical approaches often balance data availability with useful parameter estimation, such as selection strength or ancestral structure.
  • Limited data resolution can hinder the application of advanced evolutionary models.

Purpose of the Study:

  • To demonstrate a method for obtaining accurate evolutionary parameter estimates from data with limited resolution.
  • To show how to infer ancestral protein states and evolutionary parameters when complete data is unavailable.
  • To improve ancestral reconstruction and the estimation of selection strength using adapted substitution models.

Main Methods:

  • Encoding observed characters as ambiguous representations within a larger state-space.
  • Applying established methods for handling missing data to protein sequence alignments.
  • Utilizing adapted codon models (e.g., 61-state) and empirical models (e.g., 55-state) for amino acid data.

Main Results:

  • Accurate and unbiased estimation of the selection strength parameter omega ($\omega$) using an adapted 61-state codon model.
  • Successful inference of ancestral amino acid side chain configurations using a 55-state model on 20-state amino acid data.
  • Significantly improved ancestral reconstruction accuracy by incorporating structural information into even a small fraction of sequences.

Conclusions:

  • A novel strategy allows the recovery of crucial evolutionary information from protein sequences with previously inaccessible data.
  • This ambiguity-coding approach expands the applicability of sophisticated evolutionary models to lower-resolution sequence data.
  • The methods presented enhance our ability to reconstruct protein evolutionary history and understand natural selection.