A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning
A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning
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DOI：
10.1016/j.molcel.2020.04.020
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发表时间：
2020-06-04
期刊：
影响因子：
16
通讯作者：
Hahn, Steven
中科院分区：
文献类型：
--
作者：
Erijman, Ariel;Kozlowski, Lukasz;Hahn, Steven
酸性转录激活域（ADs）由一系列看似不相关的氨基酸序列编码，因此很难识别促进其动态行为、“模糊”相互作用和靶标特异性的特征。我们在酵母中随机筛选了大量具有AD功能的30聚肽，并对AD阳性和阴性序列进行了深度神经网络（ADpred）训练。ADpred识别转录因子内已知的酸性ADs，并准确预测突变的后果。我们的研究表明，强酸性的ADs在酸性侧链附近含有多个疏水残基簇，这解释了为什么ADs通常具有偏倚的氨基酸组成。ADs可能使用类似于贪婪的结合机制，激活剂和靶标之间需要最少数量的弱动态相互作用才能产生生物学相关的亲和力和体内功能。这一机制解释了酸性ADs与靶标之间观察到的模糊结合的基础。
Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy'' interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.