Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach.
Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach.
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DOI：
10.1038/s41598-018-33984-w
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发表时间：
2018-10-22
影响因子：
4.6
通讯作者：
Takeuchi I
中科院分区：
文献类型：
--
作者：
Karasuyama M;Inoue K;Nakamura R;Kandori H;Takeuchi I
微生物视紫红质的光依赖性离子转运功能已被广泛应用于光遗传学中，用于神经活动的光学控制。为了增加具有宽范围吸收波长的视紫红质蛋白的种类，在文献中研究了各种野生型视紫红质及其人工突变变体的光吸收性质。在这里，我们证明了基于机器学习（ML）的数据驱动方法对于理解和预测微生物视紫红质蛋白的光吸收特性是有用的。我们构建了一个包含796种微生物视紫红质野生型及其变体的蛋白质的数据库。然后，我们提出了一种ML方法，产生一个统计模型，描述氨基酸序列和吸收波长之间的关系，并证明了拟合的统计模型是有用的理解颜色调谐规则和预测吸收波长。通过将ML方法应用于数据库，在先前的研究中未考虑的两个残基被新确定为对色移重要。
The light-dependent ion-transport function of microbial rhodopsin has been widely used in optogenetics for optical control of neural activity. In order to increase the variety of rhodopsin proteins having a wide range of absorption wavelengths, the light absorption properties of various wild-type rhodopsins and their artificially mutated variants were investigated in the literature. Here, we demonstrate that a machine-learning-based (ML-based) data-driven approach is useful for understanding and predicting the light-absorption properties of microbial rhodopsin proteins. We constructed a database of 796 proteins consisting of microbial rhodopsin wildtypes and their variants. We then proposed an ML method that produces a statistical model describing the relationship between amino-acid sequences and absorption wavelengths and demonstrated that the fitted statistical model is useful for understanding colour tuning rules and predicting absorption wavelengths. By applying the ML method to the database, two residues that were not considered in previous studies are newly identified to be important to colour shift.
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影响因子：
62.1
作者：
Ernst, Oliver P.;Lodowski, David T.;Elstner, Marcus;Hegemann, Peter;Brown, Leonid S.;Kandori, Hideki
通讯作者：
Kandori, Hideki
影响因子：
4.4
作者：
Eichinger, M;Tavan, P;Parrinello, M
通讯作者：
Parrinello, M
影响因子：
5.5
作者：
Fujimoto, Kazuhiro;Hayashi, Shigehiko;Nakatsuji, Hiroshi
通讯作者：
Nakatsuji, Hiroshi
影响因子：
25
作者：
Lin, John Y.;Knutsen, Per Magne;Muller, Arnaud;Kleinfeld, David;Tsien, Roger Y.
通讯作者：
Tsien, Roger Y.
影响因子：
16.6
作者：
Kato, Hideaki E.;Kamiya, Motoshi;Sugo, Seiya;Ito, Jumpei;Taniguchi, Reiya;Orito, Ayaka;Hirata, Kunio;Inutsuka, Ayumu;Yamanaka, Akihiro;Maturana, Andres D.;Ishitani, Ryuichiro;Sudo, Yuki;Hayashi, Shigehiko;Nureki, Osamu
通讯作者：
Nureki, Osamu