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Modeling Structural Constraints on Protein Evolution via Side-Chain Conformational States.

Umberto Perron1, Alexey M Kozlov2, Alexandros Stamatakis2,3

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom.

Molecular Biology and Evolution
|May 23, 2019
PubMed
Summary

We developed a new protein evolution model incorporating side-chain rotamer configurations. This structurally aware model improves phylogenetic analyses and offers insights into protein folding and function.

Keywords:
molecular evolutionphylogenetic estimationphylogeneticsprotein evolutionprotein structurerotamersubstitution model

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Area of Science:

  • Biophysics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Protein structure is highly conserved and functionally crucial, yet often overlooked in sequence evolution models.
  • Advances in structural biology provide increasing data on protein structures and amino acid configurations.

Purpose of the Study:

  • To develop a novel, structurally aware empirical substitution model for amino acid sequence evolution.
  • To integrate protein side-chain rotamer configuration data into evolutionary models.
  • To assess the performance of this new model in phylogenetic analyses.

Main Methods:

  • Developed an expanded alphabet representing amino acid identity and side-chain rotamer configuration.
  • Analyzed 251,194 protein structures to assign rotamer states.
  • Identified 4,508,390 substitutions in closely related sequences.
  • Generated a 55-state "Dayhoff-like" substitution model.

Main Results:

  • Amino acid evolutionary properties are strongly influenced by side-chain geometry.
  • The 55-state structurally aware model performs comparably to or better than traditional 20-state models.
  • Demonstrated improved accuracy in divergence time estimation, tree inference, and ancestral state reconstruction.

Conclusions:

  • Protein side-chain rotamer configuration is a valuable data source for phylogenetic studies.
  • Modeling the co-evolution of protein sequence and structure has significant implications for understanding protein folding and function.
  • This approach enhances evolutionary analyses by incorporating structural information.