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首页> 外文期刊>Journal of Molecular Biology >Armadillo: Domain boundary prediction by amino acid composition
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Armadillo: Domain boundary prediction by amino acid composition

机译：犰狳：通过氨基酸组成预测域边界

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The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone.Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/- 20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods.Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions. (c) 2005 Published by Elsevier Ltd.
机译：蛋白质域的鉴定和注释为准确确定分子功能提供了关键的一步。蛋白质结构确定的计算方法和实验方法均可通过大型多域蛋白或柔性接头区域来阻止。通过允许研究人员研究一组更小且可能更成功的替代方案，对域及其边界的了解可以降低蛋白质结构测定的实验成本。当前的结构域预测方法通常依赖于与保守结构域的序列相似性，因此不适于检测保守性差或孤立的蛋白质中的结构域。我们在此提出一种简单的计算方法，仅从序列信息中识别蛋白质结构域接头及其边界。我们的结构域预测子Armadillo（http://armadillo.blueprint.org）使用任何氨基酸索引将蛋白质序列转化为平滑序列从中可以预测域和域边界的数字配置文件。我们使用非冗余结构数据集从域接头的氨基酸组成中得出了一种称为域接头倾向指数（DLI）的氨基酸指数。该指数表明Pro和Gly对接头残基表现出倾向，而小的疏水残基则没有倾向。 Armadillo通过Z分数分布预测域连接子边界，并在两域单连接子数据集中（连接子+/- 20个残基内）使用DLI获得35％的灵敏度。 DLI和基于熵的氨基酸指数的组合将两个域蛋白的整体犰狳敏感性提高到56％。此外，Armadillo对多域蛋白质的敏感性达到37％，超过了大多数其他预测方法.Armadillo提供了一种简单而有效的方法，通过该方法可以以合理的敏感性获得域边界的预测。犰狳应被证明是快速描述保守性差的蛋白质或无序列邻近蛋白质的蛋白质结构域的有价值的工具。作为一线预测器，领域元预测器可以通过Armadillo预测获得更好的结果。 （c）2005年由Elsevier Ltd.发布。

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