NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Macromolecular interaction studies on FF1-3 of human CA150 and STAT1-importin alpha5 using biophysical and biochemical methods

Macromolecular interactions, such as protein-protein interactions and protein-DNA interactions, play important roles in executing biological functions in cells. However the complexity of such interactions often makes it very challenging to elucidate the structural details of these subjects. In this thesis, two different research strategies were applied on two different two macromolecular systems: X-ray crystallography on three tandem FF domains of transcription regulator CA150 and electron microscopy on STAT1-importin α5 complex. The results from these studies provide novel insights into the function-structure relationships of transcription coupled RNA splicing mediated by CA150 and the nuclear import process of the JAK-STAT signaling pathway. ^ The first project aimed at the protein-protein interaction module FF domain, which often occurs as tandem repeats. Crystallographic structure of the first three FF domains of human CA150 was determined to 2.7 Å resolution. This is the only crystal structure of an FF domain and the only structure on tandem FF domains to date. It revealed a striking connectivity between an FF domain and the next. Peptide binding assay with the potential binding ligand of FF domains was performed using fluorescence polarization. Furthermore, for the first time, FF domains were found to potentially interact with DNA. DNA binding assays were also performed and the results were supportive to this newly proposed functionality of an FF domain. ^ The second project aimed at understanding the molecular mechanism of the nuclear import process of transcription factor STAT1. The first structural model of pSTAT1-importin α5 complex in solution was built from the images of negative staining electron microscopy. Two STAT1 molecules were observed to interact with one molecule of importin α5 in an asymmetric manner. This seems to imply that STAT1 interacts with importin α5 with a novel mechanism that is different from canonical importin α-cargo interactions. Further in vitro binding assays were performed to obtain more details on the pSTAT1-importin α5 interaction. ^