SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks.
SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks.
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DOI：
10.12688/f1000research.6773.1
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发表时间：
2015
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影响因子：
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通讯作者：
Edwards RJ
中科院分区：
文献类型：
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作者：
Olorin E;O'Brien KT;Palopoli N;Pérez-Bercoff Å;Shields DC;Edwards RJ
短线状模体(SLIMs)是一种小的蛋白质序列模式，调节着大量关键的蛋白质-蛋白质相互作用，参与复杂的形成、信号转导、定位和稳定等过程。苗条表现出快速的进化动态，经常是病原体分子模拟的目标。事实证明，识别由于非同源蛋白质中收敛的基因进化而产生的丰富序列模式是一种成功的计算细微预测策略。SLiMSuite软件包的工具使用了这一策略，通过使用统计模型，根据输入蛋白质的进化关系、氨基酸组成和预测的无序来识别苗条富集物。输入数据的质量对成功的苗条预测至关重要。Cytoscape提供了一个用户友好的交互环境来探索相互作用网络，并根据共同的特征选择蛋白质，例如共享的相互作用伙伴。SLiMScape嵌入了SLiMSuite包的工具，用于从头开始发现苗条(SLiMFinder和QSLiMFinder)，并在这个交互框架中识别已知苗条的出现/浓缩(SLiMProb)。SLiMScape使以下操作变得更容易：(1)为这些工具生成高质量的假设驱动数据集，以及(2)在网络环境中可视化预测的细微事件。*要生成新的预测，用户可以从蛋白质网络中选择节点或提供一组UniProt标识符。SLiMProb还需要额外的查询基元作为输入。然后，作业在SLiMSuite服务器(http://rest.slimsuite.unsw.edu.au))上远程运行，以便后续检索和可视化。SLiMScape还可以用于检索直接在服务器上运行的作业的结果并将其可视化。SLiMScape和SLiMSuite是开源的，并在GNU许可下通过GitHub免费提供。
Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM prediction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for de novo SLiM discovery (SLiMFinder and QSLiMFinder) and identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server ( http://rest.slimsuite.unsw.edu.au) for subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses.