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

Autor: Pavel V. Afonine, Paul D. Adams, David S. Richardson, Jane S. Richardson, Nathaniel Echols, Jeffrey J. Headd, Ralf W. Grosse-Kunstleve, Vincent B. Chen, Nigel W. Moriarty
Jazyk: angličtina
Rok vydání: 2012
Předmět:
Zdroj: Acta Crystallographica Section D: Biological Crystallography
Headd, JJ; Echols, N; Afonine, PV; Grosse-Kunstleve, RW; Chen, VB; Moriarty, NW; et al.(2012). Use of knowledge-based restraints in phenix.refine to improve macromolecular refinement at low resolution. Acta Crystallographica Section D: Biological Crystallography, 68(4), 381-390. doi: 10.1107/S0907444911047834. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/88p5c2p4
Acta crystallographica. Section D, Biological crystallography, vol 68, iss Pt 4
ISSN: 1399-0047
0907-4449
Popis: Recent developments in PHENIX are reported that allow the use of reference-model torsion restraints, secondary-structure hydrogen-bond restraints and Ramachandran restraints for improved macromolecular refinement in phenix.refine at low resolution.
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.
Databáze: OpenAIRE
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