Successful molecular-replacement phasing depends on a number of factors such as the proximity of the search model to the true structure, the quality and completeness of the diffraction data (especially at lower resolution), the solvent content, the presence of noncrystallographic symmetry and the limiting resolution (d
) of the crystals. Although recent advances in reciprocal-space refinement such as deformable elastic network (DEN) refinement (Schröder et al.
), jelly-body refinement (Murshudov et al.
) and real-space refinement (DiMaio et al.
) enable structure determination from more distant models, the ultimate success of molecular replacement phasing depends on whether previously unknown parts of the model become visible in the electron-density maps or whether conformational changes in the structure are uniquely determined.
DEN refinement consists of torsion-angle refinement interspersed with B
-factor refinement in the presence of a sparse set of distance restraints (typically one per atom, randomly selected) which are initially obtained from a reference model (Schröder et al.
). The reference model can simply be the starting model for refinement or it can be a homology or predicted model that provides external information. During the process of torsion-angle refinement with a slow-cooling simulated-annealing schema, the DEN distance restraints are adjusted in order to fit the diffraction data. The degree of this adjustment or deformation of the initial distance restraints is controlled by a parameter γ. The method of jelly-body refinement (Murshudov et al.
) bears some resemblance to the special case of DEN refinement with γ = 1. The weight of the DEN distance restraints is controlled by another parameter, w
. A two-dimensional grid search for (γ, w
) is performed in which multiple refinements for each parameter pair are performed with different initial random-number seeds for the velocity assignments of the torsion-angle molecular-dynamics method and different randomly selected DEN distance restraints. The globally optimal model (in terms of minimal R
, possibly assisted by geometric validation criteria) is then used for further refinement and model building. By default, the last two macrocycles of the DEN refinement protocol are performed without any DEN restraints, so the resulting model is not strained or biased by the reference model (although such restraints can be useful at very low resolution). In other words, the DEN restraints guide the refinement path, increasing the chances of obtaining a better model than with standard refinement. In addition, the deformability of the DEN restraints makes this method more general than rigid-body or normal-mode refinement. Thus, DEN refinement is a general refinement method that can be applied to any starting model and reference model. In practice, the reference model is likely to be identical to the starting model. However, there are situations in which the reference model can be different from the starting model. For example, re-refinements of existing structures can be performed using structures of homologous proteins that were not available at the time the original structure was determined.
A number of highly automated procedures for model building and model rebuilding have recently been developed (Levitt, 2001
; Oldfield, 2002
; Ioerger & Sacchettini, 2003
; DePristo et al.
; Cowtan, 2006
; Langer et al.
; Terwilliger et al.
). A key feature of several of these procedures is alternation between model building and calculation of electron-density maps. Each local improvement in the model leads to an overall improvement in the map, which in turn makes additional improvements in the model possible. In this work, we use one of these procedures, the AutoBuild
method (Terwilliger et al.
) as implemented in PHENIX
(Adams et al.
), as a core tool for model improvement. In one cycle of model rebuilding with AutoBuild
, a density-modified electron-density map is calculated beginning with phases from the working model and including any available experimental phase information. A new model is then built and refined with phenix.refine
(Afonine et al.
). Two methods for rebuilding the working model are used here. In the first method, several new models (or segments) are built without reference to the working model. The parts of the new models and the working model that best fit the electron-density map are then merged together to form a composite model. Using this procedure, the model can change in any way during rebuilding. In the second method, termed ‘rebuilding in place’, segments of the working model are rebuilt one at a time, maintaining connectivity and sequence alignment. This ‘rebuilding-in-place’ procedure therefore adjusts the position of existing atoms in the structure and can be thought of as an extension of refinement.
In this paper, we describe the process of determining the crystal structure of Cgl1109 (Joint Center for Structural Genomics target 376512 listed in TargetDB; http://targetdb.sbkb.org/TargetDB/
), a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum
, using a combination of molecular-replacement phasing, refinement and semi-automated model building. At the later stages, experimental phase information from SeMet MAD phasing was included in the refinement. It should be noted that these MAD phases were of insufficient quality to allow automated model building, and manual building would have been exceedingly difficult and time-consuming even for a highly skilled crystallographer (see §
3.6). Thus, molecular-replacement phasing was attempted. However, manual interpretation of the initial electron-density map again proved difficult. Indeed, Cgl1109 was one of the cases used to test the performance of real-space refinement of the molecular-replacement solution in conjunction with the Rosetta
empirical energy function (DiMaio et al.
; case 10 in Table 1 in this reference), but the refinement was not completed owing to poor or disordered density in numerous regions and low resolution (R
= 0.39; Table 1 in DiMaio et al.
Here, we present an independent structure determination of Cgl1109 at ~3 Å resolution without use of the previous Rosetta
model and molecular-replacement solution. A homology model of Cgl1109 was created using sequence alignment with PROMALS
(Pei et al.
) and modeling with MODELLER
(Sali & Blundell, 1993
) starting from the structure of succinyl-diaminopimelate desuccinylase from the β-proteobacterium Neisseria meningitidis
(PDB entry 1vgy
; Badger et al.
). The structure was determined by molecular replacement with Phaser
(McCoy et al.
) using a model edited with Sculptor
(Bunkóczi & Read, 2011
), followed by DEN refinement with a full (γ, w
) grid search (Schröder et al.
), automated model building with AutoBuild
, determination of the selenium sites by anomalous difference Fourier maps, calculation of MAD phase probability distributions using a maximum-likelihood method (Burling et al.
) and completion of the refinement in a semi-automated fashion using AutoBuild
(Adams et al.
) with the MLHL target function (Pannu et al.
). The final model has excellent geometry and R
values of 0.238 and 0.257, respectively, at 2.97 Å resolution.
This example shows that DEN refinement with a full (γ, w
) grid search generally produces models that are closer to the true structure than standard (gradient-descent) or simulated-annealing refinement methods, resulting in improved model phases and better R
values. The improved model phases in turn provide better starting points for automated model building with AutoBuild
. This approach ultimately produced a well refined structure that would have been very difficult to achieve with manual model building and standard refinement. Moreover, the improved model phases produce more significant difference peaks that better locate the anomalous diffraction selenium sites. Compared with the Rosetta
refinement method (DiMaio et al.
), DEN refinement has the advantage that it does not require extensive empirical energy-function simulations and that it has been shown to also work well for structures determined at low resolution (worse than 3.5 Å). The successful application to Cgl1109 demonstrates that DEN refinement also has significant utility for structures determined at ~3 Å resolution, especially for cases of anisotropic diffraction and/or high B
factors. The research performed in this paper also serves as a tutorial for the combined use of various methods and computer software systems to tackle difficult molecular-replacement cases. The corresponding data files have been made available on the CNS
website in the tutorial section for DEN refinement.