Dancea Felician, Günther Ulrich
Center for Biomolecular Magnetic Resonance (BMRZ), Institute of Biophysical Chemistry, J. W.Goethe-University of Frankfurt, Frankfurt am Main, Germany.
J Biomol NMR. 2005 Nov;33(3):139-52. doi: 10.1007/s10858-005-3093-1.
A major time-consuming step of protein NMR structure determination is the generation of reliable NOESY cross peak lists which usually requires a significant amount of manual interaction. Here we present a new algorithm for automated peak picking involving wavelet de-noised NOESY spectra in a process where the identification of peaks is coupled to automated structure determination. The core of this method is the generation of incremental peak lists by applying different wavelet de-noising procedures which yield peak lists of a different noise content. In combination with additional filters which probe the consistency of the peak lists, good convergence of the NOESY-based automated structure determination could be achieved. These algorithms were implemented in the context of the ARIA software for automated NOE assignment and structure determination and were validated for a polysulfide-sulfur transferase protein of known structure. The procedures presented here should be commonly applicable for efficient protein NMR structure determination and automated NMR peak picking.
蛋白质核磁共振结构测定中一个主要的耗时步骤是生成可靠的核欧沃豪斯效应光谱(NOESY)交叉峰列表,这通常需要大量的人工交互。在此,我们提出一种新的自动峰挑选算法,该算法在将峰识别与自动结构测定相结合的过程中,涉及对经小波去噪的NOESY光谱进行处理。此方法的核心是通过应用不同的小波去噪程序生成递增峰列表,这些程序会产生具有不同噪声含量的峰列表。结合用于探测峰列表一致性的附加滤波器,可以实现基于NOESY的自动结构测定的良好收敛。这些算法是在用于自动NOE归属和结构测定的ARIA软件中实现的,并针对已知结构的多硫化物 - 硫转移酶蛋白进行了验证。这里介绍的程序通常应适用于高效的蛋白质核磁共振结构测定和自动核磁共振峰挑选。