ArticleScientific reports2018
Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 68 citations in OpenAlex.
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- Accessible and robust machine learning approaches to improve the opsin genotype-phenotype map.Molecular biology and evolution · 2026Article
- Following Flavin's Vibrational Modes to Probe Anharmonicities and Low-Lying Conical Intersections.The journal of physical chemistry. B · 2025Article
- Article
- Two Decades of Optogenetic Tools: A Retrospective and a Look Ahead.Advanced genetics (Hoboken, N.J.) · 2025Review
- Blue-shifted ancyromonad channelrhodopsins for multiplex optogenetics.bioRxiv : the preprint server for biology · 2025Article
- Molecular Mechanisms Underlying the Spectral Shift in Zebrafish Cone Opsins.bioRxiv : the preprint server for biology · 2024Article
- Tetherless Optical Neuromodulation: Wavelength from Orange-red to Mid-infrared.Neuroscience bulletin · 2024Review
- Rhodopsin-based light-harvesting system for sustainable synthetic biology.Microbial biotechnology · 2024Article
- RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.Journal of chemical information and modeling · 2024Article
- Molecular mechanisms and evolutionary robustness of a color switch in proteorhodopsins.Science advances · 2024Article
- Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).GigaScience · 2024Article
- Opto-electronic feedback control of membrane potential for real-time control of action potentials.Cell reports methods · 2023Article
- Efficient model selection for predictive pattern mining model by safe pattern pruning.Patterns (New York, N.Y.) · 2023Article
- Impact of protein-chromophore interaction on the retinal excited state and photocycle ofChemical science · 2023Article
- Induction effects on the absorption maxima of photoreceptor proteins.Biophysics and physicobiology · 2023Article
- Ion-pumping microbial rhodopsin protein classification by machine learning approach.BMC bioinformatics · 2023Article
- Earliest Photic Zone Niches Probed by Ancestral Microbial Rhodopsins.Molecular biology and evolution · 2022Article
- Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol.Topics in current chemistry (Cham) · 2022Review
- Rhodopsins: An Excitingly Versatile Protein Species for Research, Development and Creative Engineering.Frontiers in chemistry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.