Marc Gouw, Hugo Sámano-Sánchez, Kim Van Roey, Francesca Diella, Toby J. Gibson, Holger Dinkel
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引用次数: 27
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Abstract
The Eukaryotic Linear Motif (ELM) resource is dedicated to the characterization and prediction of short linear motifs (SLiMs). SLiMs are compact, degenerate peptide segments found in many proteins and essential to almost all cellular processes. However, despite their abundance, SLiMs remain largely uncharacterized. The ELM database is a collection of manually annotated SLiM instances curated from experimental literature. In this article we illustrate how to browse and search the database for curated SLiM data, and cover the different types of data integrated in the resource. We also cover how to use this resource in order to predict SLiMs in known as well as novel proteins, and how to interpret the results generated by the ELM prediction pipeline. The ELM database is a very rich resource, and in the following protocols we give helpful examples to demonstrate how this knowledge can be used to improve your own research. © 2017 by John Wiley & Sons, Inc.
使用ELM数据库和工具探索短线性图案
真核线性基序(ELM)资源致力于短线性基序(SLiMs)的表征和预测。SLiMs是在许多蛋白质中发现的紧凑的退化肽段,对几乎所有细胞过程都是必不可少的。然而,尽管它们数量众多,但它们在很大程度上仍未被描述。ELM数据库是从实验文献中整理的手工注释的SLiM实例的集合。在本文中,我们将演示如何浏览和搜索数据库以获取精心策划的SLiM数据,并介绍资源中集成的不同类型的数据。我们还介绍了如何使用该资源来预测已知和新的蛋白质中的slm,以及如何解释由ELM预测管道生成的结果。ELM数据库是一个非常丰富的资源,在下面的协议中,我们提供了有用的示例来演示如何使用这些知识来改进您自己的研究。©2017 by John Wiley &儿子,Inc。
本文章由计算机程序翻译,如有差异,请以英文原文为准。