Calculation of Protein Structures with Ambiguous Distance Restraints. Automated Assignment of Ambiguous NOE Crosspeaks and Disulphide Connectivities

Abstract

The distances derived from nuclear Overhauser effect (NOE) spectra are usually converted into three-dimensional structures by computer algorithms loosely termed distance geometry. To a varying degree, these methods require that the distance data is unambiguously assigned to pairs of atoms. Typically, however, there are many NOE crosspeaks that cannot be assigned without some knowledge of the structure. These crosspeaks have to be assigned in an iterative manner, using preliminary structures calculated from the unambiguous crosspeaks. In this paper, I present an alternative to this iterative approach. The ambiguity of an NOE crosspeak is correctly described in terms of the distances between all pairs of protons that may be involved. A simple restraining term is defined in terms of “ambiguous” distance restraints that can allow all possible assignments. A new minimization procedure based on simulated annealing is described that is capable of using highly ambiguous data for ab initiostructure calculations. In particular, it is feasible to specify the restraint list directly in terms of the proton chemical shift assignment and the NOE peak table, without having assigned NOE crosspeaks to proton pairs. While the primary aim of this paper is determining the global fold of proteins from NMR data, similar strategies can be used for other types of ambiguous distance data. The application to one example, disulphide bridges with unknown connectivity, is described. Model NOE data were generated from the X-ray crystal structure of a small protein with known chemical shift assignments. Varying degrees of ambiguity in the data were assumed. The method obtained the correct polypeptide fold even when all distance restraints were ambiguous. Thus, the new approach may facilitate structure calculations with data derived from very overlapped spectra. It is also a step towards automating the calculation of structures from NMR data. This could prove especially valuable for data derived from three- and four-dimensional experiments. The approach may also prove useful for model building studies and tertiary structure prediction.