Padayatti P.S.,Polgenix, Inc. |
Wang L.,Case Western Reserve University |
Gupta S.,Case Western Reserve University |
Orban T.,Case Western Reserve University |
And 7 more authors.
Molecular and Cellular Proteomics | Year: 2013
Hybrid structural methods have been used in recent years to understand protein-protein or protein-ligand interactions where high resolution crystallography or NMR data on the protein of interest has been limited. For G protein-coupled receptors (GPCRs), high resolution structures of native structural forms other than rhodopsin have not yet been achieved; gaps in our knowledge have been filled by creative crystallography studies that have developed stable forms of receptors by multiple means. The neurotransmitter serotonin (5-hydroxytryptamine) is a key GPCR-based signaling molecule affecting many physiological manifestations in humans ranging from mood and anxiety to bowel function. However, a high resolution structure of any of the serotonin receptors has not yet been solved. Here, we used structural mass spectrometry along with theoretical computations, modeling, and other biochemical methods to develop a structured model for human serotonin receptor subtype 4(b) in the presence and absence of its antagonist GR125487. Our data confirmed the overall structure predicted by the model and revealed a highly conserved motif in the ligand-binding pocket of serotonin receptors as an important participant in ligand binding. In addition, identification of waters in the transmembrane region provided clues as to likely paths mediating intramolecular signaling. Overall, this study reveals the potential of hybrid structural methods, including mass spectrometry, to probe physiological and functional GPCR-ligand interactions with purified native protein. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 299.99K | Year: 2011
DESCRIPTION (provided by applicant): Many complex human diseases (e.g. cancer, diabetes, schizophrenia etc.) have correspondingly complex, polygenic genotypes that initiate and sustain disease progression. Despite significant progress over the past few decades identifying genes critical to mediating phenotype, our understanding of the functional basis of molecular phenotype for complex diseases is insufficient. Signaling pathways that consist of a few proteins interacting in a serial fashion oversimplify and provide inadequate models for the behavior mediated by multiple interacting gene products. Partly revealed by rigorous studies of increasingly well-annotated protein-protein interaction (PPI) networks, it has become clear that many of the proteins in these canonical signaling pathways engage in crosstalk with, and are modulated by, an ontologically diverse set of additional proteins, where this crosstalk is frequently mediated in a tissue and/or disease specific manner. We propose to develop and deliveran integrated suite of software tools to the academic and commercial research community to fulfill the unmet demand for quantitative PPI network analysis that can drive practical translational research and validation. The tool DiseaseNet Finder will search for and score candidate disease sub- networks within global PPI networks. It will permit integration of multiple high- dimensional -omic types (GWAS, SNP, CNV, proteomic, miRNA etc.) with PPI networks and include classification tools. Novel aspects of the software include: combinatorial scoring, multi data type integration, node and edge prediction tools, with end-point classification and quantitative scoring seamlessly implemented through graphical user interfaces. PUBLIC HEALTH RELEVANCE: Complex diseases include the contributions of many genes interacting with the environment. Enhanced computational research tools to discover biomarkers and understand complex disease mechanisms are needed to integrate the various types of genomic and proteomicsdata that are accumulating. This will permit a more rapid development of personalized medicine.
Our team has utilized hybrid structural methods to characterize protein-protein or protein-ligand interactions where high-resolution crystallography or NMR data are not fully available. The examples below demonstrate the power of covalent labeling methods for determining the structure of protein complexes in a variety of applications ...