Detecting Repetitions and Periodicities in Proteins by Tiling the Structural Space

TL;DR

This paper introduces analytical tools to detect and compare structural repetitions in proteins, revealing nearly periodic architectures and structural mosaics, independent of amino acid sequences, thus enhancing understanding of protein folding landscapes.

Contribution

The authors develop a robust metric-based method for identifying and analyzing structural repeats and tessellations in proteins, independent of sequence data.

Findings

Some proteins exhibit nearly periodic structures.

Clear separations between repetitions are identified in certain architectures.

The method effectively characterizes structural mosaics and repeats.

Abstract

The notion of energy landscapes provides conceptual tools for understanding the complexities of protein folding and function. Energy Landscape Theory indicates that it is much easier to find sequences that satisfy the "Principle of Minimal Frustration" when the folded structure is symmetric (Wolynes, P. G. Symmetry and the Energy Landscapes of Biomolecules. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 14249-14255). Similarly, repeats and structural mosaics may be fundamentally related to landscapes with multiple embedded funnels. Here we present analytical tools to detect and compare structural repetitions in protein molecules. By an exhaustive analysis of the distribution of structural repeats using a robust metric we define those portions of a protein molecule that best describe the overall structure as a tessellation of basic units. The patterns produced by such tessellations provide intuitive representations of the repeating regions and their association towards higher order arrangements. We find that some protein architectures can be described as nearly periodic, while in others clear separations between repetitions exist. Since the method is independent of amino acid sequence information we can identify structural units that can be encoded by a variety of distinct amino acid sequences.