Computational protein design quantifies structural constraints on amino acid covariation
Noah Ollikainen1, Tanja Kortemme
1Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America.
Plos Computational Biology
|November 19, 2013
Summary
Protein structure significantly constrains amino acid covariation, influencing protein evolution. Computational design methods that include backbone flexibility accurately capture these structural constraints, revealing key evolutionary pressures.
Area of Science:
- Structural Biology
- Computational Biology
- Protein Evolution
Background:
- Amino acid covariation, correlated amino acid identities at different sequence positions, is common in proteins.
- Covariation arises from structural, functional, and phylogenetic factors.
Purpose of the Study:
- To quantify the role of protein structure in constraining amino acid covariation.
- To assess if computational protein design can replicate natural covariation patterns.
Main Methods:
- Flexible backbone computational protein design was used on 40 protein domains.
- Amino acid covariation in natural sequences was compared to computationally designed sequences.
Main Results:
- Significant similarities were found between natural and computationally designed amino acid covariation.
- Protein structure's constraints are a dominant factor shaping amino acid covariation.
- Backbone flexibility in computational design is crucial for accurate modeling of covariation.
Conclusions:
- Computational protein design effectively captures structure-imposed amino acid covariation.
- Protein architecture plays a primary role in driving correlated amino acid changes.
- Accurate modeling requires incorporating backbone flexibility to understand evolutionary pressures.
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