Evidence map›Paper›PMID 33742139›Full record

ArticleCommunications biology2021

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.

Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura and 3 more

Erratum issuedAbstract read
In one paragraph

Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Harnessing light the retinal way.Nature chemistry · 2025
    Article
  5. Article
  6. Optogenetic engineering for ion channel modulation.Current opinion in chemical biology · 2025
    Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Rhodopsin driven microbial COEnvironmental microbiology · 2023
    Article
  17. Article
  18. Review
  19. Red Light Optogenetics in Neuroscience.Frontiers in cellular neuroscience · 2021
    Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Keiichi Inoue *The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan. inoue@issp.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0002-6898-4347
Masayuki Karasuyama *PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan.
Ryoko NakamuraDepartment of Life Science and Applied Chemistry, Nagoya Institute of Technology, Nagoya, Japan.
Masae KonnoDepartment of Life Science and Applied Chemistry, Nagoya Institute of Technology, Nagoya, Japan.
Daichi YamadaDepartment of Life Science and Applied Chemistry, Nagoya Institute of Technology, Nagoya, Japan.
Kentaro MannenThe Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan.
Takashi NagataThe Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan.
Yu InatsuDepartment of Computer Science, Nagoya Institute of Technology, Nagoya, Japan.
Hiromu YawoThe Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan.ORCID http://orcid.org/0000-0002-4299-3288
Kei YuraGraduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan.ORCID http://orcid.org/0000-0003-0197-5478
Oded BéjàFaculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
Hideki KandoriRIKEN Center for Advanced Intelligence Project, Tokyo, Japan.ORCID http://orcid.org/0000-0002-4922-1344
Ichiro TakeuchiRIKEN Center for Advanced Intelligence Project, Tokyo, Japan. takeuchi.ichiro@nitech.ac.jp.ORCID http://orcid.org/0000-0002-1366-1946

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from representative rhodopsins in the same subfamily. Among 3,022 ion-pumping rhodopsins that were suggested by a protein BLAST search in several protein databases, the ML-based method selected 65 candidate rhodopsins. The wavelengths of 39 of them were able to be experimentally determined by expressing proteins with the Escherichia coli system, and 32 (82%, p = 7.025 × 10

Indexed as

Machine LearningOptogeneticsAmino Acid SequenceBayes TheoremColorDatabases, ProteinEscherichia coliHydrogen-Ion ConcentrationIon ChannelsLightProof of Concept StudyProtein Conformation, alpha-HelicalRhodopsins, MicrobialSequence Analysis, ProteinIon ChannelsRhodopsins, Microbial

Identifiers

PMID33742139
PMCPMC7979833

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.