Interactome-wide prediction of short, disordered protein interaction motifs in humans
Interactome-wide prediction of short, disordered protein interaction motifs in humans
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DOI：
10.1039/c1mb05212h
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发表时间：
2012-01-01
影响因子：
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通讯作者：
Shields, Denis C.
中科院分区：
文献类型：
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作者：
Edwards, Richard J.;Davey, Norman E.;Shields, Denis C.
许多固有无序的蛋白质片段的特定功能是通过与其他蛋白质相互作用的短线性基序(SLIMs)来调节的。众所周知的例子包括与14-3-3、PDZ、SH2、SH3和WW结构域相互作用的SLIM，但SLIM介导的相互作用的真实程度和多样性在很大程度上是未知的。在这里，我们试图通过将计算机苗条预测应用于人类互动组来扩展我们对人类苗条的知识。结合来自七个不同相互作用数据库的数据，我们分析了大约6000个以蛋白质为中心的和1600个以结构域为中心的与共同伴侣相互作用的3+无关蛋白质的人类相互作用数据集。通过与大小和组成相似的随机数据集进行比较，将结果放在上下文中。搜索返回了数以千计的进化上保守的、本质上无序的出现在成百上千个显著丰富的反复出现的基序中，其中包括许多以前从未发现的(http://bioware.soton.ac.uk/slimdb/).除了至少25种不同的已知瘦身的真阳性结果外，数量惊人的“非靶标”蛋白质/结构域也返回了显著丰富的已知基序。通常，这是由于数据集的非独立性，许多蛋白质共享相互作用伙伴或为多个域数据集贡献相互作用。然而，这些主题类中的大多数也被发现在一个或多个随机数据集中显著丰富。这突显了在解释这种性质的基序预测时需要谨慎的必要性，但也增加了成功识别微小事件的可能性，而不是依赖于交互作用数据。尽管不像之前的研究那样在成分上有偏见，但与已知苗条匹配的模式往往会聚集成几个相似序列的大组，而新的预测往往更有特色，也不那么丰富。这是由于确定偏差还是真正的SLIMs功能组成偏差所致，尚不清楚，需要进一步研究。
Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (http://bioware.soton.ac.uk/slimdb/). In addition to True Positive results for at least 25 different known SLiMs, a striking number of "off-target" proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.