Computational methods for NMR structure determination of proteins in membranes

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DESCRIPTION (provided by applicant): This application is in response to PA-09-164 for NIH Exploratory/Developmental Research Grants. The overall goal of the project is to develop new computational methods for NMR structural analysis of integral membrane proteins within their native, functional environment of the phospholipid bilayer membrane. These protein/lipid macromolecular complexes are challenging for the conventional methods of structural biology but can be characterized using solid-state NMR spectroscopy of oriented samples. The development of computational methods is an essential aspect of this approach. The information contained in the spectra from oriented samples is unique in its ability to provide direct images of protein structures, and very different from that of NMR spectra obtained from isotropic samples. Thus, it requires a different approach for data analysis. The Specific Aims of this project are designed to take advantage of recent developments in bioinformatics, numerical methods, and molecular dynamics for simulated annealing methods. The computational methods will be developed, tested and implemented for a large body of NMR data obtained in the laboratories of the two investigators on this project, Marassi (Burnham) and Opella (UCSD), for both a-helical and ¿-barrel integral membrane proteins in membranes. These investigators will work closely with computer scientists with complementary expertise in biomolecular structure calculation, and bioinformatics and structure prediction. The Aims will be to combine the oriented sample NMR computational methods with the capabilities of the XPLOR- NIH and ROSETTA programs. PUBLIC HEALTH RELEVANCE: This research will develop and implement new computational tools for calculating the three-dimensional structures of membrane proteins from experimental NMR data. Membrane proteins are the principal class of drug receptors and this research will accelerate the discovery of drugs for many diseases.