Mapping Local Conformational Landscapes of Proteins in Solution

Abstract

The ability of proteins to adopt multiple conformational states is essential to their function, and elucidating the details of such diversity under physiological conditions has been a major challenge. Here we present a generalized method for mapping protein population landscapes by NMR spectroscopy. Experimental NOESY spectra are directly compared with a set of expectation spectra back-calculated across an arbitrary conformational space. Signal decomposition of the experimental spectrum then directly yields the relative populations of local conformational microstates. In this way, averaged descriptions of conformation can be eliminated. As the method quantitatively compares experimental and expectation spectra, it inherently delivers an R factor expressing how well structural models explain the input data. We demonstrate that our method extracts sufficient information from a single 3D NOESY experiment to perform initial model building, refinement, and validation, thus offering a complete de novo structure determination protocol.

Keywords: NMR spectroscopy; NOESY back-calculation; R factor; spectral decomposition.