Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Channel Rhodopsins

Channel Rhodopsins

Most organisms use photoreceptors to sense and respond to light. Examples of photoreceptors include bacteriorhodopsins and bacteriophytochromes in some bacteria, phytochromes in plants, and rhodopsins in the photoreceptor cells of the vertebral retina. The light-sensitive property of these receptors is because of the bound chromophores, such as bilin in the phytochromes and retinal in the rhodopsins.
Rhodopsins belong to the family of cell surface proteins called G-protein coupled receptors,...

G-Protein Gated Ion Channels

G-Protein Gated Ion Channels

GPCRs are primarily responsible for our sense of smell, taste, and vision. The binding of a sensory stimulus activates GPCR to stimulate effector proteins, many of which are ion channels in the sensory organs. GPCRs modulate the opening and closing of the target ion channels either directly by binding them, or by releasing second messengers that activate these channels. As ions move across the membrane, the membrane potential is altered, which induces an appropriate response.
Sensory...

Multi-pass Transmembrane Proteins and β-barrels

Multi-pass Transmembrane Proteins and β-barrels

In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

<i>Stagnihabitans</i><i>lacustris</i> sp. nov., an Anoxygenic Photoheterotrophic Bacterium of the Family <i>Paracoccaceae</i>, Isolated from a Eutrophic Pond in Czechia.

Microorganisms·2026

Same author

Metagenomic insights into oral microbiota dynamics in diabetic and non-diabetic periodontal disease: a pilot study.

Frontiers in microbiology·2026

Same author

Adenotonsillar Hypertrophy in Children: Surgery Improves Right Ventricular Cardiac Functions.

Clinical pediatrics·2026

Same author

Pseudomonas Pichavaramensis sp. nov., Isolated from Brackish Water of Pichavaram Mangrove forest, Tamil Nadu.

Current microbiology·2026

Same author

Gene-specific marker and trait-based evaluation of powdery mildew resistance in garden pea (Pisum sativum var Hortense L.).

Scientific reports·2026

Same author

Phenotypic diversity and molecular characterization of male sterility based on cytotype and Ms locus variation in Indian onion germplasm.

Molecular biology reports·2025

Search research articles

Related Experiment Video

Updated: Aug 12, 2025

A Rhodopsin Transport Assay by High-Content Imaging Analysis

A Rhodopsin Transport Assay by High-Content Imaging Analysis

Published on: January 16, 2019

Ion-pumping microbial rhodopsin protein classification by machine learning approach.

Muthu Krishnan Selvaraj¹, Anamika Thakur², Manoj Kumar²

¹MTCC-Microbial Type Culture Collection and Gene Bank, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR-IMTECH), Chandigarh, 160036, India.

BMC Bioinformatics

|January 28, 2023

Summary

This study developed a computational method using machine learning to accurately identify ion-pumping Haloarchaeal rhodopsins and their subtypes. This tool aids researchers in classifying these important microbial proteins for various applications.

Keywords:

Actinorhodopsin Bacteriorhodopsin Halorhodopsin Microbial rhodopsins Protein prediction Proteorhodopsin SVM Sensory rhodopsin Support vector machine Xanthorhodopsin

More Related Videos

Whole-cell Patch-clamp Recordings for Electrophysiological Determination of Ion Selectivity in Channelrhodopsins

Whole-cell Patch-clamp Recordings for Electrophysiological Determination of Ion Selectivity in Channelrhodopsins

Published on: May 22, 2017

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Published on: March 16, 2020

Related Experiment Videos

Last Updated: Aug 12, 2025

A Rhodopsin Transport Assay by High-Content Imaging Analysis

A Rhodopsin Transport Assay by High-Content Imaging Analysis

Published on: January 16, 2019

Whole-cell Patch-clamp Recordings for Electrophysiological Determination of Ion Selectivity in Channelrhodopsins

Whole-cell Patch-clamp Recordings for Electrophysiological Determination of Ion Selectivity in Channelrhodopsins

Published on: May 22, 2017

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Published on: March 16, 2020

Area of Science:

Microbial rhodopsins
Biophysics
Bioinformatics

Background:

Rhodopsins are vital seven-transmembrane proteins involved in energy conversion and signaling.
Haloarchaeal rhodopsins, a type of microbial rhodopsin, perform functions like ion pumping and phototaxis.
Current industrial applications are hindered by a lack of sequence-based classifications for ion-pumping Haloarchaeal rhodopsins.

Purpose of the Study:

To develop a cost-effective computational approach for identifying ion-pumping Haloarchaeal rhodopsin sequences and subtypes.
To create a classification system for Haloarchaeal rhodopsins to facilitate their industrial applications.

Main Methods:

Utilized Support Vector Machine (SVM) and Random Forest machine learning techniques.
Trained models on diverse haloarchaeal and marine prokaryotic ion-pumping rhodopsins (e.g., bacteriorhodopsin, proteorhodopsin).
Validated models using tenfold and five-fold cross-validation, achieving high accuracy (up to 97.78%).

Main Results:

Developed predictive models with high accuracy for classifying ion-pumping Haloarchaeal and other Type-I microbial rhodopsins.
Achieved excellent performance on independent datasets and various validation methods.
Created a web server (https://bioinfo.imtech.res.in/servers/rhodopred) for identifying these proteins and their subtypes.

Conclusions:

The developed computational method accurately identifies ion-pumping Haloarchaeal rhodopsins and their subtypes.
This tool is expected to benefit researchers in optogenetics, molecular biology, and rhodopsin studies.
Facilitates broader research and application of microbial rhodopsins.