Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.
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DOI：
10.1371/journal.pcbi.1005786
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发表时间：
2017-10
影响因子：
4.3
通讯作者：
Arnold FH
中科院分区：
文献类型：
--
作者：
Bedbrook CN;Yang KK;Rice AJ;Gradinaru V;Arnold FH
有越来越多的兴趣在研究和工程的整合膜蛋白（MP），在传感和调节细胞对各种外部信号的反应中发挥关键作用。MP必须表达，正确插入并折叠在脂质双层中，并运输到适当的细胞位置以发挥功能。这些过程的顺序和结构决定因素是复杂的，受到高度限制。在这里，我们描述了一种预测性的机器学习方法，可以捕捉这种复杂性，以促进成功的MP工程和设计。对通过结构引导的SCHEMA重组精心选择的训练序列进行机器学习，使我们能够准确预测表达和定位于哺乳动物细胞质膜的通道视紫红质（ChR）的多样化文库中的稀有序列。这些微生物来源的光门控通道蛋白对于神经科学应用是有意义的，其中在质膜上的表达和定位是功能的先决条件。我们训练高斯过程（GP）的分类和回归模型的表达和定位数据从218 ChR嵌合体选择118,098变异库设计的三个亲本ChR的SCHEMA重组。我们使用这些GP模型，以确定表达和本地化以及ChRs，并表明我们的模型可以阐明这些过程中重要的序列和结构元素。我们还使用预测模型将无法在哺乳动物中定位的天然ChR转换为定位良好的ChR。蛋白质的氨基酸序列决定了它如何折叠，运输到亚细胞位置，并在细胞内执行特定功能。理解这一过程将有助于设计具有有用功能的蛋白质序列;不幸的是，我们无法详细预测序列如何编码功能。然而，机器学习模型有可能通过从具有已知功能的序列中识别对功能重要的模式或特征来推断复杂的蛋白质序列-功能关系。我们使用机器学习来学习和设计膜蛋白（MP）。为了发挥功能，MP必须被表达，在脂质膜中正确折叠，并被运输到适当的细胞位置。我们从一组> 200个嵌合MP中为这个复杂的过程建立了预测性的机器学习模型，并使用它们来设计新序列，在膜定位的挑战性任务中具有最佳性能。这种理解和设计MP的一般方法可以广泛用于重要的制药和工程MP目标。
There is growing interest in studying and engineering integral membrane proteins (MPs) that play key roles in sensing and regulating cellular response to diverse external signals. A MP must be expressed, correctly inserted and folded in a lipid bilayer, and trafficked to the proper cellular location in order to function. The sequence and structural determinants of these processes are complex and highly constrained. Here we describe a predictive, machine-learning approach that captures this complexity to facilitate successful MP engineering and design. Machine learning on carefully-chosen training sequences made by structure-guided SCHEMA recombination has enabled us to accurately predict the rare sequences in a diverse library of channelrhodopsins (ChRs) that express and localize to the plasma membrane of mammalian cells. These light-gated channel proteins of microbial origin are of interest for neuroscience applications, where expression and localization to the plasma membrane is a prerequisite for function. We trained Gaussian process (GP) classification and regression models with expression and localization data from 218 ChR chimeras chosen from a 118,098-variant library designed by SCHEMA recombination of three parent ChRs. We use these GP models to identify ChRs that express and localize well and show that our models can elucidate sequence and structure elements important for these processes. We also used the predictive models to convert a naturally occurring ChR incapable of mammalian localization into one that localizes well. A protein’s amino acid sequence determines how it will fold, traffic to subcellular locations, and carry out specific functions within the cell. Understanding this process would enable the design of protein sequences capable of useful functions; unfortunately, we cannot predict in detail how sequence encodes function. However, machine-learning models have the potential to infer the complex protein sequence-function relationship by identifying patterns or features that are important for function from sequences with known functions. We used machine learning to learn about and design membrane proteins (MPs). To function, a MP must be expressed, correctly folded in a lipid membrane, and trafficked to the proper cellular location. We built predictive, machine-learning models for this complex process from a set of >200 chimeric MPs and used them to design new sequences with optimal performance on the challenging task of membrane localization. This general approach to understanding and designing MPs could be broadly useful for important pharmaceutical and engineering MP targets.
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