Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.
Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.
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通过机器学习对反式作用剪接因子的活动进行建模和预测。
DOI：
10.1016/j.cels.2018.09.002
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发表时间：
2018-11-28
期刊：
影响因子：
9.3
通讯作者：
中科院分区：
文献类型：
--
作者：
选择性剪接（AS）通常受反式剪接因子的调节，这些因子特异性地结合前mRNA中的顺式元件。人类基因组编码约1500种可能调节AS的RNA结合蛋白（RBP），但它们的功能在很大程度上仍然未知。为了探索它们的潜在活性，我们将RBP的推定功能结构域融合到序列特异性RNA结合结构域，并系统地分析了这些工程因子如何影响剪接。我们发现，约80%的内源性RBPs中的低复杂性结构域在调节剪接中显示出不同的上下文依赖性活性，表明AS受到比先前预期的更广泛的调节。我们开发了一种机器学习方法，根据RBP的序列组成对RBP的活性进行分类和预测，并使用内源性RBP和合成多肽进一步验证了该模型。这些结果代表了RBP序列如何影响其控制剪接的活性的系统检查、建模、预测和验证，为人工剪接因子的从头工程铺平了道路。选择性剪接主要受各种反式作用剪接因子的调控，这些因子与顺式元件特异性结合。对多种RBP的剪接调控活性进行了系统的研究，为机器学习方法预测内源性RBP和合成肽的剪接调控活性提供了训练集。这项研究扩展了潜在的剪接因子的库，并揭示了序列组成和RBP活性之间的直接联系。
Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. Human genome encodes ~1500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain largely unknown. To explore their potential activities, we fused the putative functional domains of RBPs to a sequence-specific RNA-binding domain, and systemically analyzed how these engineered factors affect splicing. We discovered that ~80% of low complexity domains in endogenous RBPs displayed distinct context-dependent activities in regulating splicing, indicating that AS is under more extensive regulation than previously expected. We developed a machine learning approach to classify and predict the activities of RBPs based on their sequence compositions, and further validated this model using endogenous RBPs and synthetic polypeptides. These results represent a systematic inspection, modeling, prediction and validation of how RBP sequences affect their activities in controlling splicing, paving the way for de novo engineering of artificial splicing factors. Alternative splicing is mainly regulated by various trans-acting splicing factors that specifically bind cis-elements. A systematic survey was conducted to study splicing regulatory activities of many RBPs, providing a training set for machine learning approach to predict splicing regulatory activities of endogenous RBPs and synthetic peptides. This study expanded the repertoire of potential splicing factors and revealed a direct link between the sequence composition and RBP activity.
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