Abstract
What factors determine a protein's rate of evolution are actively debated. Especially unclear is the relative role of intrinsic factors of present-day proteins versus historical factors such as protein age. Here we study the interplay of structural properties and evolutionary age, as determinants of protein evolutionary rate. We use a large set of one-to-one orthologs between human and mouse proteins, with mapped PDB structures. We report that previously observed structural correlations also hold within each age group – including relationships between solvent accessibility, designabililty, and evolutionary rates. However, age also plays a crucial role: age modulates the relationship between solvent accessibility and rate. Additionally, younger proteins, despite being less designable, tend to evolve faster than older proteins. We show that previously reported relationships between age and rate cannot be explained by structural biases among age groups. Finally, we introduce a knowledge-based potential function to study the stability of proteins through large-scale computation. We find that older proteins are more stable for their native structure, and more robust to mutations, than younger ones. Our results underscore that several determinants, both intrinsic and historical, can interact to determine rates of protein evolution.
Highlights
It is well known that protein evolutionary rates are not homogeneous, with as much variation within an organism as between organisms
We study how age and structural characteristics interact with one another to influence evolutionary rates
We have found an interesting interplay between structure and age: a set of structural characteristics that are correlated with evolutionary rates but in a manner that depends on protein age
Summary
It is well known that protein evolutionary rates are not homogeneous, with as much variation within an organism as between organisms. Some of the proposed determinants are correlated with one another, which makes the identification of causal factors difficult For this reason Drummond and colleagues [5] tried to disentangle these factors by performing a principal component regression (PCR) analysis. They found that a single component, which included codon adaptation index, protein abundance and gene expression level, accounted for nearly half of the observed variability in protein’s evolution. Those expression-related factors have been measured with less noise than other possible factors. They suggested there may be selection for robustness against mistranslation, since mistranslation-induced misfolding would be more deleterious for highly expressed proteins
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