Use of Knowledge-Based Restraints in phenix.refine to Improve Macromolecular Refinement at Low Resolution

Acta Crystallogr D Biol Crystallogr. 2012 Apr;68(Pt 4):381-90. doi: 10.1107/S0907444911047834. Epub 2012 Mar 16.

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).

Publication types

  • Research Support, American Recovery and Reinvestment Act
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Pairing
  • Crystallography, X-Ray / methods*
  • DNA / analysis
  • DNA / chemistry
  • Models, Molecular
  • Protein Structure, Secondary
  • Protein Structure, Tertiary
  • Proteins / analysis
  • Proteins / chemistry
  • Software*

Substances

  • Proteins
  • DNA