Incorporation of prior phase information strengthens maximum-likelihood structure refinement.

By Navraj S. Pannu, G. Murshudov, Eleanor J Dodson, Randy J. Read. 1998

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Citations in the corpus

Towards automated crystallographic structure refinement with phenix.refine (2012, background): “It can be a least-squares target (LS; for example, as defined in Afonine et al., 2005a), an amplitude-based maximum-likelihood target (ML; for example, as defined in Afonine et al., 2005a) or a phased maximum-likelihood target (MLHL; Pannu et al., 1998).”
Overview of the CCP4 suite and current developments (2011, background): “D67, 235–242 Winn et al. The CCP4 suite 239 When good-quality experimental phases are available, these can be included as additional data (Pannu et al., 1998).”
Crystal structures (2004, unknown): “MLHL likelihood (Bricogne, 1997; Murshudov et al., 1997; Pannu et al., 1998) is based on a special case of the probability distribution (3) where we have one observation, one model and phase information derived from an experiment available as a prior distribution Ppr( ), Acta Cryst.”
Application of DEN refinement and automated model building to a difficult case of molecular-replacement phasing: the structure of a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum (2012, methodology): “…automated model building as both the starting and the reference model) and automated model building was performed using the MLHL target function (Pannu et al., 1998) that included the experimental MAD phase information, resulting in relatively small localized changes in coordinates with some…”
Crystal Structure of the Cytosolic C2a-C2b Domains of Synaptotagmin III (1999, unknown): “The MLHL target function (Pannu et al., 1998) was used with the SIRAS experimental phases.”
Recent developments in phasing and structure refinement for macromolecular crystallography (2009, background): “Tests have shown that the addition of experimental phase information greatly improves the results of refinement [42].”
Crystallography & NMR system: A new software suite for macromolecular structure determination. (1998, methodology): “Thus, even complicated maximum-likelihood target func (Bricogne, 1984; Otwinowski, 1991; Pannu & Read, 1996; Pannu et al., 1998) can be defined using theCNSlanguage.”
Recent developments in classical density modification (2010, methodology): “As with the likelihood refinement target adopted by Pannu et al. (1998), the difference map (i.e. mFo DFc-like) coefficients may be obtained by calculating the gradient of the logarithm of the likelihood function (2) with respect to the calculated structure factor and adjusting the scale to match…”