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 Folding01:22

Protein Folding

129.7K
Overview
129.7K
Protein Folding01:25

Protein Folding

12.0K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
12.0K
Protein Folding01:22

Protein Folding

35.9K
35.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.9K
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...
14.9K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Proteomics01:33

Proteomics

10.0K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Hybridization-encoded DNA tags with paper-based readout for anti-forgery raw material tracking.

Nature communications·2025
Same author

Impact of a Novel Two-Phase Natural Deep Eutectic Solvent-Assisted Extraction on the Structural, Functional, and Flavor Properties of Hemp Protein Isolates.

Plants (Basel, Switzerland)·2025
Same author

Identification of Volatile Compounds in Pennycress Protein Isolates Produced by Both Alkaline and Salt-Based Processes.

Journal of agricultural and food chemistry·2025
Same author

Sub-terahertz metamaterial stickers for non-invasive fruit ripeness sensing.

Nature food·2025
Same author

Transglutaminase-Induced Polymerization of Pea and Chickpea Protein to Enhance Functionality.

Gels (Basel, Switzerland)·2024
Same author

Salt Solubilization Coupled with Membrane Filtration-Impact on the Structure/Function of Chickpea Compared to Pea Protein.

Foods (Basel, Switzerland)·2023

Related Experiment Video

Updated: Mar 13, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K

A Machine Learning Approach to Predict Functional Performance From Measurable Protein Structural Characteristics: A

Ronit Mandal1,2, Sara Malvar3, Ranveer Chandra3

  • 1Department of Food Science and Nutrition, University of Minnesota, Saint Paul, Minnesota, USA.

Proteins
|March 11, 2026
PubMed
Summary

Machine learning models accurately predict plant protein functionality, including solubility and gel strength, using key structural features. These predictive tools can guide the selection of protein ingredients for diverse food applications.

Keywords:
functional propertiesmachine learningplant proteinsregression

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
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

2.7K

Related Experiment Videos

Last Updated: Mar 13, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K
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
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

2.7K

Area of Science:

  • Food Science
  • Biochemistry
  • Computational Biology

Background:

  • The food industry increasingly uses specialized protein ingredients for gelling, thickening, and emulsifying. Protein structure significantly influences these functional properties, but the relationship is complex.
  • Understanding the link between protein structure and function is crucial for developing novel food ingredients. Traditional methods can be time-consuming and may not capture the intricate interactions.
  • Plant-based proteins are gaining traction as sustainable alternatives, necessitating efficient methods for characterizing their functionality.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting key functional properties of plant proteins.
  • To identify the most influential structural predictors for protein solubility, emulsifying activity, emulsifying capacity, and gel strength.
  • To assess the performance of ML algorithms in modeling the structure-function relationship of plant proteins.

Main Methods:

  • Utilized various ML algorithms, including Gaussian-based Support Vector Regression, to predict protein functionality.
  • Employed structural predictors such as surface hydrophobicity, zeta potential, undenatured protein content, water holding capacity, soluble protein polymer content, and β-sheet content.
  • Assessed model performance using R², Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), ensuring physical constraint non-violation.

Main Results:

  • The Gaussian-based Support Vector Regression model demonstrated high accuracy in predicting solubility (R²=0.8906), emulsifying activity index (R²=0.7383), emulsifying capacity (R²=0.7978), and gel strength (R²=0.8822).
  • Specific structural features were identified as key predictors for different functional properties: surface hydrophobicity, zeta potential, and undenatured protein content for solubility and emulsifying activity; surface hydrophobicity, solubility, and undenatured protein content for emulsifying capacity; and solubility, undenatured protein content, water holding capacity, soluble protein polymer content, and β-sheet content for gel strength.
  • The study confirmed the potential of ML for predicting plant protein functionality from a limited set of macromolecular structural characteristics.

Conclusions:

  • Machine learning algorithms offer a powerful and efficient approach to predict plant protein functionality.
  • Predictive models based on structural characteristics can significantly aid in the selection and application of protein ingredients in the food industry.
  • These ML tools can streamline ingredient development and optimize food product formulations by accurately forecasting protein behavior.