Speaker
Description
The development of novel optogenetic tools is critically dependent on engineering microbial opsins with red-shifted absorbance spectra, as red light offers superior tissue penetration and reduced phototoxicity compared to blue-green light [1]. To address the resource-intensive nature of site-directed mutagenesis, a number of in silico tools have been introduced to identify promising candidates for experimental validation [2]. However, the predictive performance of these computational tools, ranging from homology-based models to machine learning algorithms and quantum mechanical calculations, remains inadequately assessed against robust experimental datasets for channelrhodopsins [3, 4].
Our work addresses this gap by conducting a systematic comparative analysis of leading predictive tools, benchmarked against a comprehensive set of experimentally determined absorbance maxima for a library of rhodopsin mutants.
We quantitatively evaluate the accuracy, precision, and limitations of each tool in forecasting mutation-induced spectral shifts.Our findings provide practical guidelines for selecting the optimal computational tool and offer a critical cost-benefit analysis of their use, ultimately streamlining the rational design of next-generation, red-shifted rhodopsins for deep-tissue optogenetics.
This research was supported by the Ministry of Science and Higher Education of the Russian Federation (agreement # 075-03-2025-662, project FSMG-2024-00120).
References:
[1] Kimmo, et al. Red Light Optogenetics in Neuroscience, Front. Cell. Neurosci., 2022.
[2] Lingyun Zhu, et al. Protein design accelerates the development and application of optogenetic tools, Comput. Struct. Biotechnol. J., Volume 27, 2025.
[3] Masayuki, et al. Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach, Nature, 2018.
[4] Pan Q, et al. Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures, Brief. Bioinform., 2022.
