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 Folding01:25

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...

You might also read

Related Articles

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

Sort by
Same author

Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation.

IEEE/ACM transactions on computational biology and bioinformatics·2021
Same author

Double matrix completion for circRNA-disease association prediction.

BMC bioinformatics·2021
Same author

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Briefings in bioinformatics·2021
Same author

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

Computers in biology and medicine·2016
Same author

Module Based Differential Coexpression Analysis Method for Type 2 Diabetes.

BioMed research international·2015
Same author

CCAAT/enhancer-binding protein δ is a critical mediator of lipopolysaccharide-induced acute lung injury.

The American journal of pathology·2012
Same journal

Corrigendum to: Reviewing the Context of Molecular Modeling to Enhance the Application of Machine Learning Technologies for Safer Bioinformatics.

Protein and peptide letters·2026
Same journal

Corrigendum to: A Preliminary Study on the Antibacterial Activity of the Secretion of the Levantine Water Frog, <i>Pelophylax bedriagae</i> (Camerano, 1882) (Anura:Ranidae).

Protein and peptide letters·2026
Same journal

Potent Antioxidant and Antibacterial Activities of ≤3 kDa Hydrolyzed Sarcoplasmic Proteins from IPB-D1 Chicken.

Protein and peptide letters·2026
Same journal

Hybrid 3D Bioprinted Scaffolds for the Delivery of Peptide Therapeutics.

Protein and peptide letters·2026
Same journal

Targeting α-Synuclein: Current Strategies and Emerging Therapies for Synucleinopathies.

Protein and peptide letters·2026
Same journal

Biocompatible Excipients from Microalgae: Advancing Protein and Peptide Therapeutics through Sustainable Formulation Strategies.

Protein and peptide letters·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 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

Improved method for predicting phi-turns in proteins using a two-stage classifier.

Jun-Feng Xia1, Zhu-Hong You, Min Wu

  • 1Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China. jfxia@mail.ustc.edu.cn

Protein and Peptide Letters
|June 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting phi-turns, crucial protein structures. The new approach, utilizing support vector machines and advanced coding, demonstrates promising performance in identifying these elements.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Jun 12, 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

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

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

Area of Science:

  • Protein structure analysis
  • Bioinformatics
  • Computational biology

Background:

  • Phi-turns are irregular secondary structures essential for protein architecture and function.
  • Accurate prediction of phi-turns is vital for understanding protein dynamics and interactions.
  • Existing prediction methods have limitations, necessitating novel approaches.

Purpose of the Study:

  • To develop and evaluate a new, effective method for predicting phi-turns in proteins.
  • To enhance phi-turn prediction accuracy using advanced computational techniques.
  • To explore the utility of physicochemical and structural properties in phi-turn prediction.

Main Methods:

  • A two-stage classification scheme employing support vector machines (SVM) was developed.
  • Novel coding schemes incorporating protein physicochemical and structural properties were adopted.
  • A dataset of 640 non-homologue protein chains was used for evaluation.
  • Seven-fold cross-validation was implemented to assess prediction performance.

Main Results:

  • The proposed method achieved promising performance in phi-turn prediction.
  • The integration of new coding schemes proved effective.
  • The two-stage SVM classification demonstrated its capability in identifying phi-turns.

Conclusions:

  • The developed method offers an effective approach for predicting phi-turns.
  • Physicochemical and structural properties are valuable features for phi-turn prediction.
  • This work contributes to advancing computational methods in protein structure analysis.