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

Protein Organization01:24

Protein Organization

7.2K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
7.2K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

13.9K
G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
13.9K
Ligand Binding Sites02:40

Ligand Binding Sites

11.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
11.8K
Induced-fit Model01:13

Induced-fit Model

76.8K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
76.8K

You might also read

Related Articles

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

Sort by
Same author

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

International journal of biological macromolecules·2026
Same author

Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.

Briefings in bioinformatics·2026
Same author

Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.

BMC biology·2026
Same author

PlantAMP: A fine-tuned protein large language model for plant antimicrobial peptide prediction.

Plant communications·2025
Same author

From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.

Biomarker research·2025
Same author

Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Apr 22, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K

In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic

Xiaolong Li1, Xinwei Luo1, Sijia Xie1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.

Current Research in Food Science
|April 21, 2026
PubMed
Summary

Artificial intelligence (AI) accelerates the discovery of umami peptides, offering efficient screening and prediction. This review explores AI models and molecular docking for understanding umami peptide interactions with taste receptors.

Keywords:
Deep learningHomology modelingMachine learningMolecular dockingUmami peptides

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

11.9K

Related Experiment Videos

Last Updated: Apr 22, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

11.9K

Area of Science:

  • Food Science
  • Bioinformatics
  • Computational Chemistry

Background:

  • Umami peptides provide a savory taste and have significant food industry applications.
  • Traditional methods for identifying umami peptides are inefficient, costly, and slow.
  • Artificial intelligence (AI) offers advanced computational tools for efficient umami peptide screening.

Purpose of the Study:

  • To review recent advancements in AI-driven prediction of umami peptides.
  • To explore mechanistic insights into umami peptide interactions using molecular docking.
  • To guide the intelligent development and application of umami peptides in the food sector.

Main Methods:

  • Overview of umami peptide databases and feature representation techniques.
  • Summary of state-of-the-art AI models, including machine learning, deep learning, and multi-model fusion.
  • Discussion of molecular docking studies on umami peptide interactions with taste receptors (T1R1/T1R3).

Main Results:

  • AI models demonstrate high efficiency in large-scale umami peptide screening and prediction.
  • Molecular docking provides insights into the binding mechanisms of umami peptides with taste receptors.
  • Integration of AI and molecular docking supports rational design of novel umami peptides.

Conclusions:

  • AI-powered approaches significantly enhance the identification and design of umami peptides.
  • Understanding molecular interactions is crucial for optimizing umami peptide functionality.
  • Future research should focus on AI-assisted strategies for innovative food product development.