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Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

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Published on: January 16, 2016

Modeling coding-sequence evolution within the context of residue solvent accessibility.

Michael P Scherrer1, Austin G Meyer, Claus O Wilke

  • 1Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, and Section of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA.

BMC Evolutionary Biology
|September 13, 2012
PubMed
Summary
This summary is machine-generated.

Protein structure influences evolutionary conservation. A new model shows evolutionary rates (ω) vary linearly with relative solvent accessibility (RSA), improving evolutionary predictions for genes.

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07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Evolutionary biology
  • Molecular evolution
  • Bioinformatics

Background:

  • Protein structure dictates evolutionary patterns, with core residues being more conserved than surface residues.
  • Understanding these structure-sequence relationships is key to deciphering evolutionary dynamics.

Purpose of the Study:

  • To develop and test a protein sequence evolution model incorporating residue-specific relative solvent accessibility (RSA).
  • To evaluate if RSA-dependent evolutionary parameters improve model fit compared to traditional methods.

Main Methods:

  • Developed a variant of the Goldman-Yang 1994 (GY94) model where parameters are functions of RSA.
  • Applied the RSA-dependent GY94 model to a dataset of nearly 600 yeast genes.
  • Compared model performance against RSA-independent models and the Muse-Gaut 1994 (MG94) model.

Main Results:

  • An evolutionary-rate ratio (ω) that varies linearly with RSA significantly improved model fit.
  • Branch length (t) and transition-transversion ratio (κ) were also found to vary with RSA.
  • The RSA-dependent GY94 model outperformed an RSA-dependent MG94 model.

Conclusions:

  • Structure-aware evolutionary models offer superior fit compared to traditional models.
  • The linear relationship between ω and RSA suggests gene characterization benefits from slope and intercept, not just mean ω.