Discovering functional relationships among proteins using computational techniques

Recent advances in molecular biology have resulted in immense amounts of biological data especially related to proteins. Since proteins regulate and execute nearly every biological function in a cell, discovering relationships among them enables us to understand how cells function and how they have evolved.; This thesis focuses on applying database principles and computational techniques on biological data to infer functional properties of proteins. Comparison of protein structures provides a powerful method for discovery of functional and evolutionary relationships between a pair of proteins. As the sizes of experimentally determined and theoretically estimated protein structure databases grow, there is a need for scalable search techniques. This thesis presents a technique that extracts feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins and uses an index structure to answer similarity queries.; Although the study of a single structure or the alignment of a small group of structures can reveal a great deal of information, a global comprehensive view of the protein space is essential to understanding the fold similarities and the evolutionary process. Currently, a wide range of information sources is available to provide evidence about the functional relationships among proteins. Novel techniques for automatically generating the SCOP classification of a protein structure with high accuracy are proposed. One of these techniques combines the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier via a decision tree. The other one uses the global structure of the protein similarity networks and analyzes multi-attribute similarity networks by combining random walks on graphs with Bayesian theory.; Protein interaction networks provide a different view of protein functional relationships based on their interaction properties and can be used to infer intricate relations, such as pathway and complex membership. A new protein interaction network for C. elegans as well as analysis techniques for mining this interaction network to capture functional relationships among proteins based on random walks on graphs are presented.