Inhibition of pathogenic agents including α6β1 integrin receptor or α6β4 integrin receptor at a surface

Disclosed are treatment agents and methods of treatment utilizing the agents directed toward diseases in which the disease causing pathogen includes .alpha.6.beta.1 integrin receptors and/or .alpha.6.beta.4 integrin receptors on the surface of the pathogen. In one embodiment, the disease can be breast cancer. The therapeutic agents disclosed include a polypeptide comprising at least a portion of the G domain of the laminin-5 .alpha.3 chain that has been shown to bind .alpha.6.beta.1 integrin receptors and .alpha.6.beta.4 integrin receptors. In one embodiment, the therapeutic agents can be fused or chimeric materials in which the laminin-5 .alpha.3 chain polypeptide has been chemically bound to another material that can be useful in the destruction or neutralization of the pathogen

An empirical framework for binary interactome mapping

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approx130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal

Literature-curated protein interaction datasets.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.info:eu-repo/semantics/publishe