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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and
Amir Shahmoradi1, Dariya K Sydykova, Stephanie J Spielman
1Department of Physics, The University of Texas at Austin, Austin, TX, 78712, USA.
Journal of Molecular Evolution
|September 14, 2014
Summary
Simple protein structural features like buriedness and packing density effectively predict evolutionary sequence variation. These basic measures outperform complex methods derived from dynamic simulations or protein design.
Area of Science:
- Protein structure-function relationships
- Evolutionary biology
- Computational biology
Background:
- Protein structure significantly influences evolutionary sequence variation.
- Buried and highly-contacted sites tend to evolve slower than surface sites.
Purpose of the Study:
- To comprehensively assess how various structural properties predict site-specific sequence variation.
- To compare the predictive power of simple structural features against dynamic and design-based measures.
Main Methods:
- Calculated buriedness (relative solvent accessibility) and packing density (contact number).
- Assessed structural flexibility using B factors, root-mean-square fluctuations, and dihedral angle variations from molecular dynamics and homologous variants.
- Evaluated variability in designed structures using Rosetta software.
- Correlated structural properties with observed sequence variation across multiple protein structures.
Main Results:
- Most structural properties showed weak to moderate correlations (0.1-0.4) with sequence variation.
- Buriedness and packing density were superior predictors compared to structural flexibility measures.
- Variability in designed structures showed weaker predictive power than buriedness or packing density, but was comparable to flexibility measures.
Conclusions:
- Simple structural metrics like buriedness and packing density are robust predictors of evolutionary sequence variation.
- These basic measures are more effective than complex predictors from dynamic simulations, homologous ensembles, or computational design.
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