Use of knowledge-based restraints in phenix.refine to improve macromolecular refinement at low resolution

Abstract

Traditional methods for macromolecular refinement often have limited success at low resolution (3.0-3.5 Å or worse), producing models that score poorly on crystallographic and geometric validation criteria. To improve low-resolution refinement, knowledge from macromolecular chemistry and homology was used to add three new coordinate-restraint functions to the refinement program phenix.refine. Firstly, a `reference-model' method uses an identical or homologous higher resolution model to add restraints on torsion angles to the geometric target function. Secondly, automatic restraints for common secondary-structure elements in proteins and nucleic acids were implemented that can help to preserve the secondary-structure geometry, which is often distorted at low resolution. Lastly, we have implemented Ramachandran-based restraints on the backbone torsion angles. In this method, a ϕ,ψ term is added to the geometric target function to minimize a modified Ramachandran landscape that smoothly combines favorable peaks identified from nonredundant high-quality data with unfavorable peaks calculated using a clash-based pseudo-energy function. All three methods show improved MolProbity validation statistics, typically complemented by a lowered R(free) and a decreased gap between R(work) and R(free).

Figure 1

Residual plot comparing a harmonic potential with the ‘top-out’ function used for reference-model torsion restraints. For this example, l = 15.0° and σ = 1.0, which correspond to the default values in phenix.refine. The ‘top-out’ potential, shown in blue, is smoothly limited to a residual value of 225, which is equal to the value of the harmonic potential at Δ = 15.0°.

Figure 2

Reference-model side-chain examples. (a) LeuA34 from 1gtx/1ohv. The starting model in 1gtx (hot pink) is a rotamer outlier, while the corresponding side chain in 1ohv is a tp rotamer (green). After outlier correction and reference-model restraint generation, LeuA34 refines to a correct tp rotamer (dark blue). (b) GluA41 from 1gtx/1ohv. Both the starting model in 1gtx (hot pink) and 1ohv (green) are mt-10 rotamers, but the refined position with reference-model restraints in 1gtx (dark blue) is a better fit to the density. All images were generated using KiNG (Chen et al., 2009 ▶).

Figure 3

Summary of R _free improvement using reference-model restraints.

Figure 4

Clashscore for DEN test set following refinement in phenix.refine both with and without reference-model torsion restraints. As described in Chen et al. (2010 ▶), the clashscore is the number of clashes ≥0.4 Å per 1000 atoms.

Figure 5

Improvement of clashscore by using secondary-structure restraints in phenix.refine. Red, default restraints; yellow, secondary-structure restraints without outlier filtering; blue, secondary-structure restraints with outlier filtering.

Figure 6

Effect of Ramachandran statistics on structure-validation criteria for 19 low-resolution structures (Schröder et al., 2010 ▶). (a) Ramachandran percent favored and outliers. Red, default (ϕ and ψ unrestrained); yellow, monomer library uncoupled ϕ,ψ torsion restraints; green, Ramachandran restraints based on Coot (Emsley et al., 2010 ▶) and Autobondrot (Word et al., 2000 ▶); blue, harmonic Ramachandran restraints (Oldfield, 2001 ▶). Lower bars are percent of residues falling in the favored regions of the Ramachandran plot; upper (lighter and shaded) bars are percent outliers. (b) Clashscores for the same test set colored as in the first plot.