Data-driven discovery of rules for protein function classification based on sequence motifs

This thesis describes an approach to data-driven discovery of decision trees or rules for assigning protein sequences to functional families using sequence motifs. This method is able to capture regularities that can be described in terms of presence or absence of arbitrary combinations of motifs. A training set of peptidase sequences labeled with the corresponding MEROPS functional families or clans is used to automatically construct decision trees that capture regularities that are sufficient to assign the sequences to their respective functional families. The performance of the resulting decision tree classifiers is then evaluated on an independent test set. Results of experiments that proposed approach matches or outperforms protein function classification based on the presence of a single characteristic motif in terms of accuracy, precision, and recall. We compared the rules constructed using motifs generated by a multiple sequence alignment based motif discovery tool (MEME) with rules constructed using expert annotated ProSite motifs (patterns and profiles). Our results indicate that the former provide a potentially powerful high throughput technique for constructing protein function classifiers when adequate training data are available. Examination of the generated rules in the case of a Caspase (C14) family suggests that the proposed technique might be able to identify combinations of sequence motifs that characterize functionally significant 3-dimensional structural features of proteins

Molecular Evolutionary Studies using Structural Genomics and Proteomics.

The field of molecular evolution has progressed with the accumulation of various molecular data. It started with the analysis of protein sequence data, followed by that of gene and genome sequence dada. Recently, structural genomics and proteomics have offered new types of data for addressing molecular evolution questions. Structural genomics refers to genome-wide collection of protein structures, whereas proteomics is the study of all proteins in a cell or organism. In this thesis, I conducted molecular evolutionary projects using data provided by structural genomics and proteomics. First, I used protein structure information to explain why some human-disease associated amino acid residues (DARs) appear as the wild-type in other species. Because destabilizing protein structures is a primary reason why DARs are deleterious, I focused on protein stability and discovered that, in species where a DAR represents the wild-type, the destabilizing effect of the DAR is generally lessened by the observed amino acid substitutions in the spatial proximity of the DAR. This finding of compensatory residue substitutions has important implications for understanding epistasis in protein evolution. Second, the recently published human proteomes include peptides encoded by annotated pseudogenes, which are relics of formerly functional genes. These translated pseudogenes may actually be functional and subject to purifying selection. Alternatively, their translations may be accidental and do not indicate functionality. My analysis suggests that a sizable fraction of the translated pseudogenes are subject to purifying selection acting at the protein level. Third, for the purpose of understanding protein evolution and structure-function relationships, protein structures are classified according to their structure similarities. A fold encompasses protein structures with similar core topologies. Current fold classifications implicitly assume that folds are discrete islands in the protein structure space, whereas increasing evidence supports a continuous fold space. I developed a likelihood method to classify structures into existing folds by considering the continuity in fold space. My results using this method demonstrated the growing importance of considering this continuity in fold classification. Together, my work illustrated the utility of structural genomics and proteomics in answering evolutionary questions and provided better understanding of gene and protein evolution.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113597/1/jinruixu_1.pd

Efficacy of different protein descriptors in predicting protein functional families using support vector machine

Master'sMASTER OF SCIENCE (PHARMACY

CHARACTERIZATION OF THE E3 UBIQUITIN LIGASE SIAH2 AS AN ANTI-CANCER TARGET

Ph.DDOCTOR OF PHILOSOPH