Specifying, Identifying, and Comparing Higher-Order Protein Structures: Theory, Algorithm, and Tools

Abstract

Secondary structures are polypeptide chains folded into 3D conformations which forms the backbone of all proteins. Secondary structures form the foundation of structural motifs, which are integral in the understanding of structure-function relationships within proteins, as common motifs provide critical information regarding a protein’s function, suggesting the prediction of function from basic structural patterns. In this thesis, it was shown that secondary structures could be defined with algorithms using consistent objective patterns found within the structures. These algorithms could furthermore be implemented in Python resulting in the development of 19 unique classification algorithms capable of determining the objective characteristics. Recognizing that different structural patterns may be of interest to different researchers with various needs, a system was designed, and a prototype was provided that would share these structural patterns within a community, as well as enable researchers to possibly refine the described sets and explore the categories using set operations.