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 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.
Protein Organization01:13

Protein Organization

Overview
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

You might also read

Related Articles

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

Sort by
Same author

Drug-resistant tuberculosis in Lishui, China: first-line drug resistance patterns, trends, and risk factors from a 10-year retrospective study (2015-2024).

Frontiers in public health·2026
Same author

Parental perspectives and willingness towards digital paediatric research: a cross-sectional survey in two Chinese cities.

BMJ open·2026
Same author

Disrupted light exposure is associated with cardiac remodeling involving clock gene alterations and oxidative-inflammatory responses in rats.

Scientific reports·2026
Same author

Thrombotic outcomes and mortality with roxadustat for anemia in chronic kidney disease: a systematic review and meta-analysis of randomized trials.

Frontiers in pharmacology·2026
Same author

Observer-based output-feedback stabilization of nonlinear systems with periodically event-triggered sampled-output-data.

ISA transactions·2026
Same author

Synergistic gellan gum/flaxseed gum composite enhances freeze-thaw stability of soy isolate protein emulsion gels.

Food chemistry·2026

Related Experiment Video

Updated: Jul 4, 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

Efficient ensemble schemes for protein secondary structure prediction.

Kun-Hong Liu1, Jun-Feng Xia, Xueling Li

  • 1Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China.

Protein and Peptide Letters
|June 10, 2008
PubMed
Summary

This study introduces an efficient ensemble system for protein secondary structure prediction using neural networks. The multi-layer ensemble approach enhances prediction accuracy, especially with more precise base classifiers.

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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

Related Experiment Videos

Last Updated: Jul 4, 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 WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in structural biology

Background:

  • Accurate protein secondary structure prediction is crucial for understanding protein function and structure.
  • Neural networks have shown promise as base classifiers for this task.
  • Ensemble methods can potentially improve prediction performance by combining multiple models.

Purpose of the Study:

  • To propose and evaluate an efficient ensemble system for protein secondary structure prediction.
  • To investigate the impact of multi-layer architectures on prediction accuracy.
  • To assess the benefit of using more accurate base classifiers within the ensemble.

Main Methods:

  • Development of an ensemble system utilizing neural networks as base classifiers.
  • Implementation of a multi-layer system architecture.
  • Experimental evaluation of the proposed ensemble system on protein secondary structure prediction tasks.

Main Results:

  • The proposed multi-layer ensemble system achieves improved prediction accuracy compared to single classifiers.
  • Higher accuracy of the ensemble system is observed when employing more accurate base classifiers.
  • The ensemble approach demonstrates the effectiveness of combining multiple neural network models.

Conclusions:

  • The developed ensemble system offers an efficient and effective solution for protein secondary structure prediction.
  • Multi-layer ensemble designs and the use of accurate base classifiers are key to achieving high prediction performance.
  • This work contributes to advancing computational methods in structural bioinformatics.