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MFR 片段组装的改进算法。

An improved algorithm for MFR fragment assembly.

机构信息

Max F. Perutz Laboratories, Department of Structural and Computational Biology, Centre for Molecular Biology, University of Vienna, Campus Vienna Biocenter 5, 1030 Vienna, Austria.

出版信息

J Biomol NMR. 2012 Jun;53(2):149-59. doi: 10.1007/s10858-012-9632-7. Epub 2012 May 13.

Abstract

A method for generating protein backbone models from backbone only NMR data is presented, which is based on molecular fragment replacement (MFR). In a first step, the PDB database is mined for homologous peptide fragments using experimental backbone-only data i.e. backbone chemical shifts (CS) and residual dipolar couplings (RDC). Second, this fragment library is refined against the experimental restraints. Finally, the fragments are assembled into a protein backbone fold using a rigid body docking algorithm using the RDCs as restraints. For improved performance, backbone nuclear Overhauser effects (NOEs) may be included at that stage. Compared to previous implementations of MFR-derived structure determination protocols this model-building algorithm offers improved stability and reliability. Furthermore, relative to CS-ROSETTA based methods, it provides faster performance and straightforward implementation with the option to easily include further types of restraints and additional energy terms.

摘要

提出了一种从仅含骨架的 NMR 数据生成蛋白质骨架模型的方法,该方法基于分子片段替换(MFR)。在第一步中,使用实验性仅含骨架数据(即骨架化学位移(CS)和残差偶极耦合(RDC))从 PDB 数据库中挖掘同源肽片段。其次,对该片段库进行实验约束的细化。最后,使用 RDC 作为约束,使用刚体对接算法将片段组装成蛋白质骨架折叠。为了提高性能,可以在此阶段包含骨架核 Overhauser 效应(NOE)。与以前的 MFR 衍生结构确定协议的实现相比,该建模算法提供了更高的稳定性和可靠性。此外,与基于 CS-ROSETTA 的方法相比,它提供了更快的性能和简单的实现,并可以轻松地包含其他类型的约束和附加能量项。

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