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Review
. 2012 Jun;22(3):316-25.
doi: 10.1016/j.sbi.2012.05.001. Epub 2012 May 24.

The use of evolutionary patterns in protein annotation

Angela D Wilkins  1 , Benjamin J Bachman, Serkan Erdin, Olivier Lichtarge
Affiliations
Review

The use of evolutionary patterns in protein annotation

Angela D Wilkins et al. Curr Opin Struct Biol. 2012 Jun.

Abstract

With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence--the defining features of biological systems--and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.

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Figures

Figure 1
Figure 1
Evolutionary approaches to characterize protein function rely on both global or local patterns. These include global sequence similarity (Homology) and local residue conservation (Motifs), or global structural similarity (Fold Recognition) and local structural similarity (3D Templates). Another pattern is evolutionary classification (Phylogenomics). The Evolutionary Trace (ET) combines these approaches by defining key structural or functional positions based on whether their evolutionary variations couple to small (blue, where the breaks between rectangles indicate residue variations), or large evolutionary divergences (red). Top-ranked positions typically map out functional sites to guide targeted mutations or extract functional motifs, such as for 3D templates. Proteome-wide 3D template matches between structures give rise to a proteomic network that can be analyzed for global function prediction.
Figure 2
Figure 2
Distribution of the statistical clustering z-score of ET residues in 10417 proteins from the PDB90. This z-score is the difference between the observed and the expected random clustering pattern in units of standard deviation. A z-score can be obtained at any ET coverage of a protein. This histogram shows the maximum clustering z-score between 0% to 50% coverage, which is representative of z-scores over most of this interval. The high values (94% with z-score > 2) show that evolutionarily important residues cluster together in the protein, as a general rule.
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