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

Journal Article (Journal Article)

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).

Full Text

Duke Authors

Cited Authors

  • Headd, JJ; Echols, N; Afonine, PV; Grosse-Kunstleve, RW; Chen, VB; Moriarty, NW; Richardson, DC; Richardson, JS; Adams, PD

Published Date

  • April 2012

Published In

Volume / Issue

  • 68 / Pt 4

Start / End Page

  • 381 - 390

PubMed ID

  • 22505258

Pubmed Central ID

  • PMC3322597

Electronic International Standard Serial Number (EISSN)

  • 1399-0047

Digital Object Identifier (DOI)

  • 10.1107/S0907444911047834


  • eng

Conference Location

  • United States