Dataset Information

Reaction dynamics of the chimeric channelrhodopsin C1C2.

ABSTRACT: Channelrhodopsin (ChR) is a key protein of the optogenetic toolkit. C1C2, a functional chimeric protein of Chlamydomonas reinhardtii ChR1 and ChR2, is the only ChR whose crystal structure has been solved, and thus uniquely suitable for structure-based analysis. We report C1C2 photoreaction dynamics with ultrafast transient absorption and multi-pulse spectroscopy combined with target analysis and structure-based hybrid quantum mechanics/molecular mechanics calculations. Two relaxation pathways exist on the excited (S1) state through two conical intersections CI1 and CI2, that are reached via clockwise and counter-clockwise rotations: (i) the C13=C14 isomerization path with 450 fs via CI1 and (ii) a relaxation path to the initial ground state with 2.0?ps and 11?ps via CI2, depending on the hydrogen-bonding network, hence indicating active-site structural heterogeneity. The presence of the additional conical intersection CI2 rationalizes the relatively low quantum yield of photoisomerization (30?±?3%), reported here. Furthermore, we show the photoreaction dynamics from picoseconds to seconds, characterizing the complete photocycle of C1C2.

SUBMITTER: Hontani Y

PROVIDER: S-EPMC5543136 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Reaction dynamics of the chimeric channelrhodopsin C1C2.

Hontani Yusaku Y Marazzi Marco M Stehfest Katja K Mathes Tilo T van Stokkum Ivo H M IHM Elstner Marcus M Hegemann Peter P Kennis John T M JTM

Scientific reports 20170803 1

Channelrhodopsin (ChR) is a key protein of the optogenetic toolkit. C1C2, a functional chimeric protein of Chlamydomonas reinhardtii ChR1 and ChR2, is the only ChR whose crystal structure has been solved, and thus uniquely suitable for structure-based analysis. We report C1C2 photoreaction dynamics with ultrafast transient absorption and multi-pulse spectroscopy combined with target analysis and structure-based hybrid quantum mechanics/molecular mechanics calculations. Two relaxation pathways ex ...[more]

PMID: 28775289

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Project description:BackgroundThere has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper methods for model selection and assessment. Here, we have addressed this issue by introducing a novel and general framework, the C1C2, for simultaneous model selection and assessment. The framework relies on a partitioning of the data in order to separate model choice from model assessment in terms of used data. Since the number of conceivable models in general is vast, it was also of interest to investigate the employment of two automatic search methods, a genetic algorithm and a brute-force method, for model choice. As a demonstration, the C1C2 was applied to simulated and real-world datasets. A penalized linear model was assumed to reasonably approximate the true relation between the dependent and independent variables, thus reducing the model choice problem to a matter of variable selection and choice of penalizing parameter. We also studied the impact of assuming prior knowledge about the number of relevant variables on model choice and generalization error estimates. The results obtained with the C1C2 were compared to those obtained by employing repeated K-fold cross-validation for choosing and assessing a model.ResultsThe C1C2 framework performed well at finding the true model in terms of choosing the correct variable subset and producing reasonable choices for the penalizing parameter, even in situations when the independent variables were highly correlated and when the number of observations was less than the number of variables. The C1C2 framework was also found to give accurate estimates of the generalization error. Prior information about the number of important independent variables improved the variable subset choice but reduced the accuracy of generalization error estimates. Using the genetic algorithm worsened the model choice but not the generalization error estimates, compared to using the brute-force method. The results obtained with repeated K-fold cross-validation were similar to those produced by the C1C2 in terms of model choice, however a lower accuracy of the generalization error estimates was observed.ConclusionThe C1C2 framework was demonstrated to work well for finding the true model within a penalized linear model class and accurately assess its generalization error, even for datasets with many highly correlated independent variables, a low observation-to-variable ratio, and model assumption deviations. A complete separation of the model choice and the model assessment in terms of data used for each task improves the estimates of the generalization error.

Dataset Information

Reaction dynamics of the chimeric channelrhodopsin C1C2.

Publications

Reaction dynamics of the chimeric channelrhodopsin C1C2.

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