A flexible and efficient procedure for the solution and phase refinement of protein structures.

An ab initio method is described for solving protein structures for which atomic resolution (better than 1.2 A) data are available. The problem is divided into two stages. Firstly, a substructure composed of a small percentage ( approximately 5%) of the scattering matter of the unit cell is positioned. This is used to generate a starting set of phases that are slightly better than random. Secondly, the full structure is developed from this phase set. The substructure can be a constellation of atoms that scatter anomalously, such as metal or S atoms. Alternatively, a structural fragment such as an idealized alpha-helix or a motif from some distantly related protein can be orientated and sometimes positioned by an extensive molecular-replacement search, checking the correlation coefficient between observed and calculated structure factors for the highest normalized structure-factor amplitudes |E|. The top solutions are further ranked on the correlation coefficient for all E values. The phases generated from such fragments are improved using Patterson superposition maps and Sayre-equation refinement carried out with fast Fourier transforms. Phase refinement is completed using a novel density-modification process referred to as dynamic density modification (DDM). The method is illustrated by the solution of a number of known proteins. It has proved fast and very effective, able in these tests to solve proteins of up to 5000 atoms. The resulting electron-density maps show the major part of the structures at atomic resolution and can readily be interpreted by automated procedures.

Is single-wavelength anomalous scattering sufficient for solving phases? A comparison of different methods for a 2.1 A structure solution.

The structure of rusticyanin is the largest unknown structure (M(r) = 16.8 kDa) which has been recently solved by the direct-methods approach using only single-wavelength anomalous scattering (SAS) data from the native protein [Harvey et al. (1998). Acta Cryst. D54, 629-635]. Here, the results of the Sim distribution approach [Hendrickson & Teeter (1981). Nature (London), 290, 107-113] and of the CCP4 procedure MLPHARE [Collaborative Computational Project, Number 4 (1994). Acta Cryst. D50, 760-763] are compared with those from direct methods. Analysis against the final refined model shows that direct methods produced significantly better phases (average phase error 56 degrees ) and therefore significantly better electron-density maps than the Sim distribution and MLPHARE approaches (average phase error was around 63 degrees in both cases).