Skip to main content
Open Access Publications from the University of California

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

  • Author(s): Headd, JJ
  • Echols, N
  • Afonine, PV
  • Grosse-Kunstleve, RW
  • Chen, VB
  • Moriarty, NW
  • Richardson, DC
  • Richardson, JS
  • Adams, PD
  • et al.

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 Ψ term is added to the geometric target function to minimize a modified Ramachandran landscape that smoothly combines favorable peaks identified from non-redundant 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 Rfreeand a decreased gap between Rworkand Rfree. © International Union of Crystallography 2012.

Main Content
Current View