Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.
Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.
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DOI：
10.1371/journal.pcbi.1010999
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发表时间：
2023-03
影响因子：
4.3
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中科院分区：
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多亚单位RNA聚合酶的催化和保真度依赖于一个高度保守的活性部位结构域，称为触发环(Trigger loop，TL)，它通过构象变化和与NTP底物的相互作用在转录中发挥作用。TL残基的突变会对催化产生不同的影响，包括活性低下和活性过高，以及保真度改变。我们应用分子动力学模拟(MD)和机器学习(ML)技术对酿酒酵母RNA聚合酶II(POL II)系统中的TL突变进行了研究。我们这样做是为了确定个体突变和表型之间的关系，并将表型与MD模拟的结构变化联系起来。利用突变体在不同胁迫条件下的适应值，我们沿着一系列连续的值对表型进行建模。我们发现ML仅根据氨基酸序列就可以预测表型，相关性为0.68 R2。结合MD数据来改进机器学习的预测更加困难，这可能是因为MD数据太过嘈杂，而且可能不完整，无法直接推断功能表型。然而，基于MD数据的变分自动编码器模型允许基于结构细节对具有不同表型的突变进行聚类。总体而言，我们发现功能丧失(LOF)和致死突变的一个子集倾向于增加TL残基到NTP底物的距离，而另一个LOF和致死替换子集倾向于增加TL和桥螺旋(BH)之间的距离。相反，一些功能增益(GOF)突变体似乎导致TL和附近螺旋之间的疏水接触中断。RNA聚合酶II(POL II)在被称为触发环(TL)的活性部位结构域的帮助下合成RNA。TL的突变导致POL II活性的变化，范围从功能获得(GOF，可存活但过度活跃)到功能丧失(LOF，可存活但低活性)或致死。本研究利用分子动力学(MD)模拟和机器学习(ML)对TL突变的结构和功能结果进行了系统的表征。我们使用遗传适合度分数(衡量生长缺陷强度的指标)作为输入，通过ML获得突变体的功能表型。我们发现，突变的TL序列可以相对较高的相关性预测功能结果。然后，我们进行了MD模拟，将结构信息与表型联系起来。对MD数据的分析表明，存在两个致命性和LOF突变体子集，其中一个子集增加了TL和底物之间的距离，而另一个子集显示TL和另一个称为桥式螺旋(BH)的活性部位结构域之间的距离增加。另一方面，一些GOF突变体改变了活性部位附近残基之间相互作用形成的关键疏水口袋。总体而言，这项研究加强了我们对TL突变对POL II功能影响的理解。
Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices. RNA polymerase II (Pol II) synthesizes RNA with the help of an active site domain called the trigger loop (TL). Mutations in the TL cause changes in the activity of Pol II that range from gain-of-function (GOF, viable but hyperactive) to loss-of-function (LOF, viable but hypoactive) or lethal. This study provides a systematic characterization of the structural and functional outcomes of the TL mutations using molecular dynamics (MD) simulations and machine learning (ML). We obtained functional phenotypes of mutants by ML using genetic fitness scores (measure of growth defect strength) as input. We revealed that mutant TL sequences could predict the functional outcomes at a relatively high correlation. Then, we performed MD simulations to relate structural information to the phenotypes. The analysis of the MD data suggested that there are two subsets of lethal and LOF mutants, where one subset had increased distances between the TL and the substrate, while the other subset showed increased distances between TL and another active site domain called the bridge helix (BH). On the other hand, some of the GOF mutants altered a key hydrophobic pocket formed by interactions between residues near the active site. Overall, this study enhances our understanding of the effects of TL mutations to the Pol II function.
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