Zhang Chong, Wang Qinghua, Ma Jianpeng
Applied Physics Program, Rice University, Houston, TX 77005, USA.
Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Acta Crystallogr D Biol Crystallogr. 2015 Nov;71(Pt 11):2150-7. doi: 10.1107/S139900471501528X. Epub 2015 Oct 27.
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.
在大分子X射线晶体学中，基于低分辨率实验衍射数据构建更精确的原子模型仍然是一项巨大挑战。先前的研究使用了可变形弹性网络（DEN）模型来辅助低分辨率结构精修。在本研究中，报告了一种称为可变形复合网络（DCN）的新精修算法的开发，该算法在目标函数中将基于新型角度网络的约束与DEN模型相结合。在广泛的低分辨率结构上对DCN进行测试表明，根据多个精修标准判断，它始终能显著改善结构模型，因此代表了一种用于低分辨率结构测定的新的有效精修工具。
