Research Article: Gyre and gimble: a maximum-likelihood replacement for Patterson correlation refinement

Date Published: April 01, 2018

Publisher: International Union of Crystallography

Author(s): Airlie J. McCoy, Robert D. Oeffner, Claudia Millán, Massimo Sammito, Isabel Usón, Randy J. Read.

http://doi.org/10.1107/S2059798318001353

Abstract

Maximum-likelihood rigid-body refinement can be carried out to improve oriented models before the translation-function step of molecular replacement.

Partial Text

Brünger’s Patterson correlation (PC) refinement (Brünger, 1990 ▸) ascertained the value of breaking a molecular-replacement search model into smaller components and performing a refinement step between the traditional molecular-replacement rotation and translation functions at the point where only the orientation, but not the position, of the model is known. The principle of PC refinement is to take a list of possible orientations of a model, determined from a rotation function, divide the model into appropriate components, and then refine the orientation angles and relative translation coordinates of the components against the Patterson correlation target function (i.e. the correlation coefficient on structure-factor intensities). Starting separately from each of the orientations in the list, PC refinement itself may increase the signal of the rotation search sufficiently to make the correct orientation stand out from the noise, or with the rigid bodies correctly oriented and positioned relative to one another, the signal in the translation search may be much improved. PC refinement was first implemented in X-PLOR (Brünger, 1992 ▸) and subsequently in CNS (Brünger et al., 1998 ▸), and has been highly cited in the crystallographic literature (Harzing & van der Wal, 2008 ▸). A brute-force search using the PC target was implemented in BRUTE (Fujinaga & Read, 1987 ▸).

We chose the solution of the Fab(26-10)–digoxin complex using Fab HyHel-5 as a model to test gyre and gimble (Brünger, 1991 ▸). The Fab(26-10) structure is deposited in the Protein Data Bank as PDB entry 1igj, with experimental data representing the twinned data described in Brünger (1991 ▸), whereas the data distributed with CNS are detwinned (Brünger, 1991 ▸; Jeffrey et al., 1993 ▸). We chose to use the detwinned data, as these were used in the original study, but rather than truncating the data at different resolutions, we used variation of the estimated r.m.s.d. between the model and target to give different resolution-dependent weighting of the structure factors in the likelihood function.

Gyre refinement has been incorporated into ARCIMBOLDO_SHREDDER (Sammito et al., 2014 ▸; Millán et al., 2018 ▸). ARCIMBOLDO_SHREDDER performs highly parallel and systematic molecular-replacement searches using a library of small structure motifs derived from a homologous structure (Sammito et al., 2013 ▸) and analyses the results to extract information from the persistence of solutions for different fragments among the noisy rotation-function results from Phaser. Potential molecular-replacement solutions are passed to SHELXE (Sheldrick, 2010 ▸) for density modification and model building, with the prospect that any correctly placed fragments can be expanded into a full structure.

Hinge motions between domains may still confound molecular replacement, because it is not possible to simultaneously overlay all domains in the model on the target. The molecular-replacement signal is degraded both by the smaller fraction scattering of the total that can be superposed on the target and by the noise introduced by the necessity of incorrectly placing a substantial fraction of the atoms. When there is a hinge motion between the model and target, Phaser frequently finds several different mutually exclusive solutions, where different combinations of domains are correctly overlaid on the target or, for small hinge motions, a solution that represents a compromise fit of all domains to the target. These solutions, although in some way correct, can be challenging to carry forward to model building and refinement; phenix.morph_model (Terwilliger et al., 2013 ▸) and REFMAC’s jelly-body refinement (Murshudov et al., 2011 ▸) can be very helpful in this regard.

 

Source:

http://doi.org/10.1107/S2059798318001353

 

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