分辨率(逻辑)
低分辨率
结晶学
功能(生物学)
集合(抽象数据类型)
互易晶格
计算机科学
高分辨率
算法
物理
化学
生物
人工智能
光学
地质学
程序设计语言
衍射
进化生物学
遥感
作者
Frank DiMaio,Nathaniel Echols,Jeffrey J. Headd,Thomas C. Terwilliger,Paul D. Adams,David Baker
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2013-09-29
卷期号:10 (11): 1102-1104
被引量:183
摘要
Refinement of macromolecular structures against low-resolution crystallographic data is limited by the ability of current methods to converge on a structure with realistic geometry. We developed a low-resolution crystallographic refinement method that combines the Rosetta sampling methodology and energy function with reciprocal-space X-ray refinement in Phenix. On a set of difficult low-resolution cases, the method yielded improved model geometry and lower free R factors than alternate refinement methods.
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