Automated refinement of macromolecular structures at low resolution using prior information. Issue 10 (9th November 2016)

• Record Type: Journal Article
• Authors: Kovalevskiy, Oleg; Nicholls, Robert A.; Murshudov, Garib N.
• Is Part Of: Acta crystallographica. Volume 72:Issue 10(2016)
• Page Start: 1149
• Page End: 1161
• Publication Date: 2016-11-09
• Subjects: REFMAC5 -- ProSMART -- low‐resolution refinement -- external restraints -- LORESTR; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798316014534
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : An automated pipeline for low‐resolution structure refinement ( LORESTR ) has been developed to assist in the hassle‐free refinement of difficult cases. The pipeline automates the selection of high‐resolution homologues for external restraint generation and optimizes the parameters for ProSMART and REFMAC 5, improving R factors and geometry statistics in 94% of the test cases. Abstract : Since the ratio of the number of observations to adjustable parameters is small at low resolution, it is necessary to use complementary information for the analysis of such data. ProSMART is a program that can generate restraints for macromolecules using homologous structures, as well as generic restraints for the stabilization of secondary structures. These restraints are used by REFMAC 5 to stabilize the refinement of an atomic model. However, the optimal refinement protocol varies from case to case, and it is not always obvious how to select appropriate homologous structure(s), or other sources of prior information, for restraint generation. After running extensive tests on a large data set of low‐resolution models, the best‐performing refinement protocols and strategies for the selection of homologous structures have been identified. These strategies and protocols have been implemented in the Low‐Resolution Structure Refinement ( LORESTR ) pipeline. The pipeline performs auto‐detection of twinning and selects the optimal scaling method and solvent parameters. LORESTR can either use user‐supplied homologous structures, or run an automated BLAST search and download homologues from the PDB. The pipeline executes multiple model‐refinement instances using different parameters in order to find the best protocol. Tests show that the automated pipeline improves R factors, geometry and Ramachandran statistics for 94% of the low‐resolution cases from the PDB included in the test set.