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.

RNA binding characteristics and overall topology of the vaccinia virus polyadenylation complex

mRNA 3′ polyadenylation is central to mRNA biogenesis in prokaryotes and eukaryotes, and is implicated in numerous aspects of mRNA metabolism, including efficiency of mRNA export from the nucleus, message stability, and initiation of translation. However, due to the great complexity of the eukaryotic polyadenylation apparatus, the mechanisms of RNA 3 ′ end processing have remained elusive. Although the RNA processing reactions leading to polyadenylated messenger RNA have been studied in many systems, and much progress has been made, a complete understanding of the biochemistry of the poly(A) polymerase enzyme is still lacking. My research uses Vaccinia virus as a model system to gain a better understanding of this complicated polyadenylation process, which consist of RNA binding, catalysis and polymerase translocation. ^ Vaccinia virus replicates in the cytoplasm of its host cell, so it must employ its own poly(A) polymerase (PAP), a heterodimer of two virus encoded proteins, VP55 and VP39. VP55 is the catalytic subunit, adding 30 adenylates to a non-polyadenylated RNA in a rapid processive manner before abruptly changing to a slow, non-processive mode of adenylate addition and dissociating from the RNA. VP39 is the stimulatory subunit. It has no polyadenylation catalytic activity by itself, but when associated with VP55 it facilitates the semi-processive synthesis of tails several hundred adenylates in length. ^ Oligonucleotide selection and competition studies have shown that the heterodimer binds a minimal motif of (rU)2 (N)25 U, the “heterodimer binding motif”, within an oligonucleotide, and its primer selection for polyadenylation is base-type specific. ^ Crosslinking studies using photosensitive uridylate analogs show that within a VP55-VP39-primer ternary complex, VP55 comes into contact with all three required uridylates, while VP39 only contacts the downstream uridylate. Further studies, using a backbone-anchored photosensitive crosslinker show that both PAP subunits are in close proximity to the downstream −10 to −21 region of 50mer model primers containing the heterodimer binding motif. This equal crosslinking to both subunits suggests that the dimerization of VP55 and VP39 creates either a cleft or a channel between the two subunits through which this region of RNA passes. ^ Peptide mapping studies of VP39 covalently crosslinked to the oligonucleotide have identified residue R107 as the amino acid in close proximity to the −10 uridylate. This helps us project a conceptual model onto the known physical surface of this subunit. In the absence of any tertiary structural data for VP55, we have used a series of oligonucleotide selection assays, as well as crosslinking, nucleotide transfer assays, and gel shift assays to gain insight into the requirements for binding, polyadenylation and translocation. Collectively, these data allow us to put together a comprehensive model of the structure and function of the polyadenylation ternary complex consisting of VP39, VP55 and RNA. ^