Detecting repetitions and periodicities in proteins by tiling the structural space.

PARRA, RG; ESPADA, R; SÁNCHEZ IE; SIPPL M; FERREIRO, DU

Berlín

Congreso; ISMB/ECCB; 2013

International Society for Computational Biology

Background 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. Similarly, repeats and structural mosaics may be fundamentally related to landscapes with multiple embedded funnels. The mere existence of repetitions does not guarantee that the system will be symmetric as these should arrange in particular ways and coalesce into higher order patterns. Detecting repeated units and patterns is a first step towards an understanding of their assembly to complete structures and the emergence of symmetry. Since the same structural motif can be encoded by sequences that appear unrelated sequence-based methods usually fail to detect them. Up to date there is no method to fully detect and define repeats from structure. Description: 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 tessellation of basic units. Patterns produced by such tessellations provide intuitive representations of the repeating regions and their association towards higher order arrangements. 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 with high sequence variability. Conclusion: We have designed a method that is completely structurally driven and capable to detect repeated structural patters by aplying an exhaustive tessellation algorithm. Our method is able to univocally define individual repeats in protein structures and also to analyze their coalescence in higher orders that could be related to functional insights. We have defined a scoring function that may represent an overall measure of symmetry in protein structures allowing to protein structures comparison on the same ground. Because of the algorithm definition our analysis are independent of the overall protein topology allowing for protein structures comparison on the same grounds.