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DomAns - pattern based method for protein domain boundaries prediction and analysis

机译:基于DomAns-基于模式的蛋白质域边界预测和分析方法

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摘要

Determination of the native folded structure for a particular protein is a milestone towards understanding its function, and in most cases, can be done experimentally. However, the ability to predict in silico protein structure and related features would represent a fundamental breakthough in structural biology. The ability to predict domains in proteins is amongst the most important tasks needed for efective functional classification, homology-based structure prediction, structural genomics, as it makes function prediction easier. In this paper, we present the DomAnS, protein domain prediction approach, that is based on pattern alignment. DomAnS allows rapid screening for potential domain regions with the ability to recognize the most promising regions where domains might exists. The combination of the DomAnS algorithm with specialized databases that contains all known domains, allows us to find domain regions without solving 3D structure. Our approach has been tested on CASP7 data, and for 28 targets gave the best overall score.
机译:确定特定蛋白质的天然折叠结构是了解其功能的一个里程碑,并且在大多数情况下,可以通过实验完成。然而,预测计算机蛋白质结构和相关特征的能力将代表结构生物学的根本突破。预测蛋白质结构域的能力是有效功能分类,基于同源性的结构预测,结构基因组学所需的最重要任务之一,因为它使功能预测更容易。在本文中,我们提出了基于模式比对的DomAnS蛋白域预测方法。 DomAnS可以快速筛选潜在的域区域,并能够识别可能存在域的最有希望的区域。 DomAnS算法与包含所有已知域的专用数据库的结合,使我们无需查找3D结构即可查找域区域。我们的方法已经在CASP7数据上进行了测试,并且针对28个目标给出了最佳总体评分。

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