Open AccessDeformable complex network for refining low‐resolution X‐ray structures
Author(s) -
Zhang Chong,
Wang Qinghua,
Ma Jianpeng
Publication year - 2015
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
ISSN - 1399-0047
DOI - 10.1107/s139900471501528x
Subject(s) - resolution (logic) , low resolution , computer science , range (aeronautics) , algorithm , function (biology) , angular resolution (graph drawing) , diffraction , network structure , network model , artificial intelligence , high resolution , optics , materials science , physics , mathematics , machine learning , geology , remote sensing , combinatorics , evolutionary biology , composite material , biology
In macromolecular X‐ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low‐resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network‐based restraint with the DEN model in the target function. Testing of DCN on a wide range of low‐resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low‐resolution structural determination.