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一种预测跨膜螺旋中断的新颖非统计方法。

A novel, non-statistical method for predicting breaks in transmembrane helices.

作者信息

Nikiforovich G V

机构信息

Center for Molecular Design, Institute for Biomedical Computing, Washington University, St Louis, MO 63110, USA.

出版信息

Protein Eng. 1998 Apr;11(4):279-83. doi: 10.1093/protein/11.4.279.

Abstract

We have developed a novel, non-statistical procedure for predicting possible breaks in transmembrane helices based on energy calculations. The procedure consists of stepwise elongation of the 'core' helical fragment determined by consensus results of several available prediction procedures. At each step, we calculate the conformational energies corresponding to the regular 'frozen' helical conformer of the 'core' fragment elongated by two flanking residues, E(alpha), as well as those to several options for the fragment to enter or exit the helix by changing conformations of the flanking residues, Ei. The minimal values out of Ei - E(alpha), delta(k), can be viewed as a profile of relative energies, where each minimum of delta(k) is a signal to start or to stop transmembrane helix. We suggest that boundaries of the transmembrane helix would be determined by the signals closest to the 'core' sequence in the delta(k) profiles. Our procedure was applied to prediction of the N- and C-termini for 45 transmembrane helices from the photosynthetic reaction center from Rhodopseudomonas viridis, bacteriorhodopsin and the cytochrome c oxidase from Paracoccus denitrificans. The results clearly showed that it is significantly more probable that a prediction accuracy within an error of +/- 2 residues will be obtained by our procedure than by three different statistical approaches.

摘要

我们基于能量计算开发了一种全新的、非统计性的程序,用于预测跨膜螺旋中可能的断裂点。该程序包括逐步延长由几种现有预测程序的共识结果确定的“核心”螺旋片段。在每一步中,我们计算对应于由两个侧翼残基延长的“核心”片段的规则“冻结”螺旋构象的构象能量E(α),以及片段通过改变侧翼残基的构象进入或退出螺旋的几种选择的能量Ei。Ei - E(α)中的最小值delta(k)可被视为相对能量分布,其中delta(k)的每个最小值都是开始或停止跨膜螺旋的信号。我们认为跨膜螺旋的边界将由delta(k)分布中最接近“核心”序列的信号确定。我们的程序被应用于预测来自绿硫红假单胞菌光合反应中心、细菌视紫红质和反硝化副球菌细胞色素c氧化酶的45个跨膜螺旋的N端和C端。结果清楚地表明,与三种不同的统计方法相比,我们的程序获得误差在+/- 2个残基内的预测准确性的可能性要大得多。

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