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首页> 外文期刊>Journal of Molecular Biology >Predicting metal-binding site residues in low-resolution structural models
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Predicting metal-binding site residues in low-resolution structural models

机译：在低分辨率结构模型中预测金属结合位点残基

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摘要

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%.The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site. (C) 2004 Elsevier Ltd. All rights reserved.
机译：鉴于结构和序列数据库的空前增长，准确预测蛋白质的生化功能变得越来越重要。因此，需要一种计算方法来以自动化方式分析此类数据，以确保准确注释基因组。蛋白质结构预测方法，例如，能够在全基因组范围内生成近似的结构模型。但是，在这样的原始模型中检测功能上重要的区域以及结构基因组学目标仍然是一个极为重要的问题。当前研究中描述的方法MetSite代表了一种全自动方法，用于检测适用于中等质量蛋白质模型的金属结合残基簇。该方法涉及将序列概况信息与近似结构数据结合使用。几种神经网络分类器能够区分金属位点和非金属位点，平均准确度为94.5％。该方法被证明可以在LiveBench目标中正确识别金属结合位点，而使用这些方法无法检测到明显的金属结合序列基序InterPro。对于没有使用侧链信息的mGenTHREADER生成的低分辨率预测结构，准确检测金属位点是可行的。观察到最近解决的流感嗜血杆菌假说蛋白的高分预测，表明推测的金属结合位点。 （C）2004 Elsevier Ltd.保留所有权利。

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