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

Overview
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

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...
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...
Protein Organization01:13

Protein Organization

Overview
Protein Organization01:24

Protein Organization

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.

You might also read

Related Articles

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

Sort by
Same author

The dual effects of carboxymethyl cellulose on the colloidal stability and toxicity of nanoscale zero-valent iron.

Chemosphere·2015
Same author

Rapid degradation of sulphamethoxazole and the further transformation of 3-amino-5-methylisoxazole in a microbial fuel cell.

Water research·2015
Same author

Metal-free methylation of a pyridine N-oxide C-H bond by using peroxides.

Organic & biomolecular chemistry·2015
Same author

[Research Progress in Norovirus Bioaccumulation in Shellfish].

Bing du xue bao = Chinese journal of virology·2015
Same author

Comparison of Shoulder Management Strategies after Stage I of Fingertip Skin Defect Repair with a Random-Pattern Abdominal Skin Flap.

Medical science monitor : international medical journal of experimental and clinical research·2015
Same author

Anti-inflammatory and Anti-oxidative Activities of Paeonol and Its Metabolites Through Blocking MAPK/ERK/p38 Signaling Pathway.

Inflammation·2015

Related Experiment Video

Updated: May 30, 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

Protein 8-class secondary structure prediction using conditional neural fields.

Zhiyong Wang1, Feng Zhao, Jian Peng

  • 1Toyota Technological Institute at Chicago, 6045 S Kenwood, Chicago, IL 60637, USA.

Proteomics
|August 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel probabilistic method for predicting 8-class protein secondary structure (SS) using conditional neural fields (CNFs). The CNF approach significantly improves prediction accuracy compared to existing methods.

More Related Videos

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Related Experiment Videos

Last Updated: May 30, 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

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in bioinformatics

Background:

  • Predicting protein secondary structure (SS) into 8 classes is more challenging than 3-class prediction, particularly for proteins with limited sequence homologs.
  • Existing methods for 8-class SS prediction often lack sufficient accuracy.

Purpose of the Study:

  • To develop a new probabilistic method for accurate 8-class protein secondary structure (SS) prediction.
  • To leverage conditional neural fields (CNFs) for modeling complex sequence-SS relationships and residue interdependencies.

Main Methods:

  • Utilized conditional neural fields (CNFs), a probabilistic graphical model, for 8-class SS prediction.
  • Incorporated sequence profiles and non-evolutionary information into the prediction model.
  • Exploited interdependencies among SS types of adjacent residues.

Main Results:

  • Achieved Q8 accuracy of 64.9% on the CB513 dataset and 64.7% on the RS126 dataset.
  • Demonstrated significantly improved performance compared to the SSpro8 web server (51.0% and 48.0% accuracy, respectively).

Conclusions:

  • The proposed CNF method offers a substantial advancement in 8-class protein SS prediction accuracy.
  • The method's flexibility allows for predicting other protein structure properties and RNA SS.