Understanding how protein structures evolve is essential for deciphering relationships between homologous proteins, which can inform structure classification and function annotation and aid in protein modeling and design methods. The observation that structure is more conserved than sequence, over the course of evolution, implies a model of evolution where sequences diverge within a discrete set of well-defined folds, which suggests that homology does not exist across fold definitions. However, as more structures have been experimentally solved and the coverage of the universe of folds has increased, the original view of a discrete fold space has been revised to include a more nuanced view of a continuous space defined by regions in which structural similarities can connect globally disparate topologies.
Structural, functional and evolutionary relationships are known to, in some cases, span fold definitions. A hallmark of relationships connecting disparate topologies is the conservation of local structure motifs within globally different folds. In order to systematically identify and analyze these relationships, a new approach to structure comparisons and structure classification is required. The goal of this work is to systematically identify evolutionary relationships between folds and to generate a classification of the fold universe that can accurately represent even the relationships that exist across disparate topologies.
An exhaustive library of supersecondary-structure motifs (Smotif), defined as two secondary structures connected by a loop, is established and characterized. A novel Smotif-based, superposition-independent structure comparison method (SmotifCOMP) is developed that quantitatively measures the Smotif-based similarity of compared structures in order to identify evolutionary relationships. SmotifCOMP is able to provide a quantitative and robust measure of similarity between disparate topologies since it does not rely on a global superposition. The comparison method is used to perform a systematic analysis of the SCOP Superfamilies and generate a non-hierarchical, network-based representation of the fold universe.
This thesis describes the development of a novel method of comparing structures and an improved representation of the relationships within the fold space. This work provides insight into the existence of evolutionary relationships between folds and strengthens the view of a connected and continuous fold universe.
|School Location:||United States -- New York|
|Source:||DAI-B 77/04(E), Dissertation Abstracts International|
|Subjects:||Molecular biology, Bioinformatics, Biophysics|
|Keywords:||Protein Fold, Protein Fold Evolution, Protein Structure, Protein Structure Classifications, Protein Structure Comparison, Protein Structure Motifs|
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