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分子动力学信息提高基于顺式肽的蛋白质功能注释。

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

机构信息

Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India.

出版信息

J Proteome Res. 2017 Aug 4;16(8):2936-2946. doi: 10.1021/acs.jproteome.7b00217. Epub 2017 Jul 6.

DOI:10.1021/acs.jproteome.7b00217
PMID:28633522
Abstract

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

摘要

顺式肽键在蛋白质中很少见但在进化上是保守的,它们被认为在蛋白质功能中发挥着重要作用。这导致了它们以前在一种不依赖同源性的、基于片段匹配的蛋白质功能注释方法中的应用。然而,蛋白质不是静态的分子;动力学是其活性的组成部分。这很好地体现在顺式肽键的几何异构化为分子活性的反式形式上。因此,我们结合了静态(顺式肽键)和动态信息,以提高蛋白质分子功能的预测。我们的结果表明,在存在顺反异构化但仅获得反式异构体的 3D 坐标或顺式肽键被错误分配为反式的情况下,顺式肽键信息本身无法检测到功能匹配;而单独使用动力学信息会包含假阳性匹配,因为具有相似二级结构的片段表现出相似的动力学,但这些蛋白质没有共同的功能。两种方法的结合减少了错误,同时检测到真正的匹配,从而增强了我们方法在功能注释中的实用性。因此,联合方法为改进现有的自动化功能注释方法开辟了新的途径。

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