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.

We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model efficiently. However, to add and/or remove multiple data points, the computational cost of current update algorithm becomes inhibitive because we need to repeatedly apply it for each data point. In this paper, we develop an extension of incremental decremental algorithm which efficiently works for simultaneous update of multiple data points. Some analyses and experimental results show that the proposed algorithm can substantially reduce the computational cost. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.

Experimental Bayesian optimization for Li ion conductivity in NASICON-type LiZr2(PO4)3.