Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.9K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.9K
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

3.8K
ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
3.8K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Phase II Reactions: Glutathione Conjugation and Mercapturic Acid Formation01:22

Phase II Reactions: Glutathione Conjugation and Mercapturic Acid Formation

365
Glutathione, a tripeptide made up of glutamate, cysteine, and glycine, is a critical player in the detoxification of drugs and xenobiotics via a process known as glutathione conjugation or mercapturic acid formation. This phase II biotransformation reaction involves the covalent binding of glutathione to a drug or its metabolite, enhancing the compound's water solubility and enabling its excretion.
Several distinctive characteristics distinguish glutathione conjugation from other phase II...
365
Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.3K
Translocation of Proteins into the Mitochondria01:19

Translocation of Proteins into the Mitochondria

3.2K
Mitochondrial precursors are translocated to the internal subcompartments via independent mechanisms involving distinct protein machineries called translocases.
Sorting of outer membrane proteins:
Mitochondrial outer membrane proteins are of two types: the transmembrane, beta-barrel porins, and the membrane-anchored, alpha-helical proteins. Beta-barrel porin precursors are translocated by the TOM complex and inserted into the outer mitochondrial membrane by the SAM complex. In contrast,...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features.

PloS one·2025
Same author

Identification of Key Genes Associated with Overall Survival in Glioblastoma Multiforme Using TCGA RNA-Seq Expression Data.

Genes·2025
Same author

Parkinson's disease screening using a fusion of gait point cloud and silhouette features.

PloS one·2025
Same author

Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results.

Hypertension research : official journal of the Japanese Society of Hypertension·2023
Same author

Eight-color multiparameter flow cytometry (EuroFlow-NGF) is as sensitive as next-generation sequencing in detecting minimal/measurable residual disease in autografts of patients with multiple myeloma.

EJHaem·2023
Same author

Rare toxin A-negative and toxin B-positive strain of Clostridioides difficile from Japan lacking a complete tcdA gene.

Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy·2022

Related Experiment Video

Updated: Sep 7, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

486

ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation

Fatma Indriani1,2, Kunti Robiatul Mahmudah3, Bedy Purnama4

  • 1Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.

Frontiers in Genetics
|June 17, 2022
PubMed
Summary

Identifying lysine glutarylation sites is crucial. Our new computational model, ProtTrans-Glutar, combines sequence features with advanced AI to rapidly predict glutarylation sites, improving upon existing methods.

Keywords:
binary classificationimbalanced data classificationlysine glutarylationmachine learningpost-translation modificationprotein embeddingprotein sequencetransformer-based models

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.1K

Related Experiment Videos

Last Updated: Sep 7, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

486
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Modeling an Enzyme Active Site using Molecular Visualization Freeware
14:37

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

10.1K

Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Lysine glutarylation is a vital post-translational modification (PTM) regulating biological processes.
  • Current proteomic methods for identifying glutarylation are costly and slow.

Purpose of the Study:

  • To develop an efficient computational model for predicting lysine glutarylation sites.
  • To enhance the speed and accuracy of glutarylation site identification in protein sequences.

Main Methods:

  • Proposed ProtTrans-Glutar model integrating traditional sequence features (composition/transition/distribution encoding, enhanced amino acid composition) with transformer-based protein model features (ProtT5-XL-UniRef50).
  • Employed random under-sampling and XGBoost classification algorithm for model training and validation.

Main Results:

  • The ProtTrans-Glutar model achieved a recall of 0.7864, specificity of 0.6286, and AUC of 0.7075 on an independent test set.
  • Demonstrated superior recall and AUC scores compared to previous models on the same dataset.

Conclusions:

  • ProtTrans-Glutar offers a rapid and effective approach for identifying lysine glutarylation sites.
  • The model's high recall suggests potential for discovering novel glutarylation sites, advancing research in this PTM.