[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Eukaryotic cells contain diverse subcellular organelles. These organelles form distinct functional cellular compartments where different biological processes and functions are carried out. The accurate translocation of a protein is crucial to establish and maintain cellular organization and function. Newly synthesized proteins are transported to different cellular components with the assistance of protein transport machineries and complex targeting signals. Mis-localization of proteins is often associated with metabolic disorders and diseases. Compared with experimental methods, computational prediction of protein localization, utilizing different machine learning methods, provides an efficient and effective way for studying the protein subcellular localization on the whole-proteome level. Here, we present in this dissertation the bioinformatics methods for studying protein subcellular localization. We reviewed the studies of protein subcellular transport and machine learning methods in bioinformatics, presented our work on mitochondrial protein targeting prediction in plants, summarized the ongoing development of a web-resource for protein subcellular localization, and discussed the future work and development.
Computational prediction of RNA tertiary structures using machine learning methods
