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

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

You might also read

Related Articles

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

Sort by

Same author

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same author

RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural Networks.

ACS synthetic biology·2026

Same author

MPMFMol: Multitask Self-Supervised Pretraining with Multimodal Fine-Tuning for Molecular Property Prediction.

Journal of chemical information and modeling·2026

Same author

Quantum computing applications in drug discovery.

Briefings in bioinformatics·2026

Same author

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.

Briefings in bioinformatics·2026

Same author

Deep learning the TF regulatory code for gene expression.

Genome research·2026

Search research articles

Related Experiment Video

Updated: Nov 5, 2025

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Published on: June 14, 2017

ORS-Pred: An optimized reduced scheme-based identifier for antioxidant proteins.

Changli Feng¹, Haiyan Wei², Deyun Yang¹

¹Department of Information Science and Technology, Taishan University, Taian, China.

|May 19, 2021

Summary

This study introduces a computational model to quickly identify antioxidant proteins, crucial for cell protection against free radical damage. The developed method achieves high accuracy, offering a valuable tool for researchers.

Keywords:

antioxidant protein machine learning protein classifier the reduced scheme

More Related Videos

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Published on: June 21, 2021

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Published on: March 24, 2012

Related Experiment Videos

Last Updated: Nov 5, 2025

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Published on: June 14, 2017

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Published on: June 21, 2021

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Published on: March 24, 2012

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Antioxidant proteins play a vital role in cellular defense by neutralizing free radicals.
Rapid and accurate identification of antioxidant proteins is essential for understanding cellular protection mechanisms.

Purpose of the Study:

To develop a computational model for the rapid identification of antioxidant proteins.
To optimize a recoding scheme and machine learning approach for protein classification.

Main Methods:

Collected over 600 recoding schemes to build a comprehensive set.
Recoded protein sequences into reduced expressions using g-gap dipeptides (g=0, 1, 2) as features.
Employed a random forest (RF) classifier with a grid search for parameter optimization.

Main Results:

The optimized recoding scheme and RF model achieved high accuracy in identifying antioxidant proteins, with recognition rates between 90.13% and 99.87%.
Experimental results validated the efficiency and predictive performance of the developed method.
A freely accessible web server was created for researchers to utilize the identification tool.

Conclusions:

The proposed computational model, utilizing optimized recoding schemes and machine learning, is highly effective for identifying antioxidant proteins.
This approach provides an efficient and accessible tool for biological research.
The developed web server facilitates broader application and discovery in the field.