基于结构和序列的非同源蛋白功能预测。

Lee Sael, Meghana Chitale, Daisuke Kihara
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引用次数: 34

摘要

结构基因组学项目已经积累了越来越多的蛋白质结构,其中许多仍然是功能未知的。在与实验方法并行的努力中,计算方法有望为这类蛋白质的功能阐明做出重大贡献。然而,同源蛋白传递函数的传统计算方法对这些未表征的蛋白质结构没有太大帮助,因为它们与已知蛋白质没有明显的结构或序列相似性。本文简要介绍了计算函数预测的两种方法,即基于结构的方法和基于序列的方法。重点介绍了基于局部结构和序列的方法的最新进展,这些方法可以有效地从远亲蛋白中提取功能信息。两种基于结构的方法,pocket - surfer和Patch-Surfer,在不使用全局蛋白质折叠相似性信息的情况下,识别出查询蛋白中口袋区域相似的已知配体结合位点。两种基于序列的方法,蛋白质功能预测和扩展相似群,利用了弱相似序列,这些序列通常在基于同源性的函数注释中被丢弃。我们希望计算方法能与实验方法相结合,在蛋白质结构的功能解析中发挥主导作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structure- and sequence-based function prediction for non-homologous proteins.

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

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