Use of knowledge-based restraints in phenix.refine to improve macromolecular refinement at low resolution.
Acta Crystallogr D Biol Crystallogr
; 68(Pt 4): 381-90, 2012 Apr.
Article
en En
| MEDLINE
| ID: mdl-22505258
ABSTRACT
Traditional methods for macromolecular refinement often have limited success at low resolution (3.0-3.5â
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 Ï,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).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Cristalografía por Rayos X
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Acta Crystallogr D Biol Crystallogr
Año:
2012
Tipo del documento:
Article
País de afiliación:
Estados Unidos