Recent developments in classical density modification

Abstract

Classical density-modification techniques (as opposed to statistical approaches) offer a computationally cheap method for improving phase estimates in order to provide a good electron-density map for model building. The rise of statistical methods has lead to a shift in focus away from the classical approaches; as a result, some recent developments have not made their way into classical density-modification software. This paper describes the application of some recent techniques, including most importantly the use of prior phase information in the likelihood estimation of phase errors within a classical density-modification framework. The resulting software gives significantly better results than comparable classical methods, while remaining nearly two orders of magnitude faster than statistical methods.

Figure 1

The terms F _o, F _c, s and ω describe the observed and calculated structure factor, the scale factor and the radius of the Gaussian error term in the Argand diagram. The shading represents the Gaussian probability distribution centred on sF _c.

Figure 2

Mean map correlation calculated of a range of JCSG data sets with using different density-modification programs and options. (a) Parrot with no new features compared with DM. (b) Parrot with MLHL likelihood function compared with the Rice function. (c) As (b), with anisotropy correction compared with no anisotropy correction. (d) As (c), with automated NCS averaging compared with no NCS averaging.