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一种用于从低分辨率X射线数据和较差初始模型自动构建蛋白质模型的新型MR-SAD算法。

A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model.

作者信息

Skubák Pavol, Araç Demet, Bowler Matthew W, Correia Ana R, Hoelz Andre, Larsen Sine, Leonard Gordon A, McCarthy Andrew A, McSweeney Sean, Mueller-Dieckmann Christoph, Otten Harm, Salzman Gabriel, Pannu Navraj S

机构信息

Department of Biophysical Structural Chemistry, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.

Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637, USA.

出版信息

IUCrJ. 2018 Jan 25;5(Pt 2):166-171. doi: 10.1107/S2052252517017961. eCollection 2018 Mar 1.

Abstract

Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F-ATPase data set and a 4.5 Å resolution SecYEG-SecA complex data set. All of the models were automatically built by the method to values of between 28.9 and 39.9% and were free from the initial model bias.

摘要

从分辨率低于3 Å的X射线数据中确定大分子结构仍然是一项挑战。即使有相关的起始模型,其不完整性或偏差以及低观测与参数比可能导致该过程不成功或非常耗时。然而,许多具有生物学重要性的大分子,特别是大型大分子组装体、膜蛋白和受体,往往会提供衍射到低分辨率的晶体。本文提出了一种新算法来解决这个问题,该算法使用多变量函数同时利用初始部分模型和低分辨率单波长反常衍射数据中的信息。这种新方法已用于六个具有挑战性的结构测定,包括膜蛋白和大分子复合物的晶体结构,这些结构用其他方法一直难以解决,以及来自3.0 Å分辨率的F-ATPase数据集和4.5 Å分辨率的SecYEG-SecA复合物数据集的大型结构。所有模型均通过该方法自动构建,R值在28.9%至39.9%之间,且不存在初始模型偏差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce40/5947721/ec138279fd6f/m-05-00166-fig1.jpg

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