Improved Structure Refinement Through Maximum Likelihood

By Navraj S. Pannu, Randy J. Read. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Towards automated crystallographic structure refinement with phenix.refine (2012, background): “In maximum-likelihood (ML)-based refinement (Pannu & Read, 1996; Bricogne & Irwin, 1996; Murshudov et al., 1997; Adams et al., 1997; Pannu et al., 1998) the calculation of the ML target (Lunin & Urzhumtsev, 1984; Read, 1986, 1990; Lunin & Skovoroda, 1995) requires an estimation of model error…”
Metrics for comparison of crystallographic maps (2014, methodology): “These data are used for validation (Brünger, 1992) and to estimate statistical parameters (Lunin & Skovoroda, 1995; Pannu & Read, 1996; Murshudov et al., 1997).”
A glimpse of structural biology through X-ray crystallography. (2014, methodology): “Free R factor was also instrumental to implementation of maximum likelihood target functions in crystallographic programs (Pannu and Read, 1996).”
Checking your imagination: applications of the free R value. (1996, background): “It should also be noted that cross-validation can be applied to any statistic, such as likelihood [6, 7 ] or phase error [8].”
Direct incorporation of experimental phase information in model refinement. (2004, result): “…et al., 1997) and compared with the `Rice' likelihood function lacking prior phase information (Bricogne & Irwin, 1996; Murshudov et al., 1997; Pannu & Read, 1996), denoted below as Rice, and the likelihood function encoding prior phase information with Hendrickson±Lattman coef®cients (Pannu…”
Modelling dynamics in protein crystal structures by ensemble refinement (2012, unknown): “Ensemble refinement of 20 datasets from the PDB We performed MD simulations, in which the model was restrained by a time-averaged X-ray ( Gros et al., 1990 ), maximum-likelihood ( Pannu and Read, 1996 ; Adams et al., 1997 ; Murshudov et al., 1997 ) target function (see ‘Materials and methods’).”
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): “The MLF target function (Pannu & Read, 1996) was used for the refinement against the diffraction data at the inflection point (the same diffraction data that were used in the work by DiMaio et al., 2011).”
Simulated-annealing real-space refinement as a tool in model building. (2002, methodology): “Realspace re®nement was followed by phase-restrained maximumlikelihood (Murshudov et al., 1997; Pannu & Read, 1996) Acta Cryst.”
From DNA sequence to improved functionality: using protein sequence comparisons to rapidly design a thermostable consensus phytase. (2000, methodology): “…was done against all data in the resolution range 30.0–2.9 Å with the program CNS 0.3c (β-test version, kindly provided by A.T.Bruenger) using a maximum-likelihood target function (Pannu and Read, 1996) and conventional minimization with ideal stereochemistry parameters from Engh and Huber (1991).”
Structure quality and target parameters (2006, methodology): “Note that this simplified treatment of parameter uncertainty can be implemented more rigorously in maximum-entropy refinement methods (Pannu & Read, 1996; Bricogne, 1993).”
Detecting outliers in non-redundant diffraction data. (1999, methodology): “The MLF target (Pannu & Read, 1996) was used for coordinate re®nement, which was followed by restrained B-factor re®nement and another round of coordinate re®nement.”
Recent developments in phasing and structure refinement for macromolecular crystallography (2009, methodology): “Over the past 10 years there have been great improvements in the targets for refinement of incomplete, errorcontaining models by making use of the more general maximum-likelihood formulation [39,40].”