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

Undetectable Hydroxyurea Levels in the Majority of Sickle Cell Disease Patients, Especially in Young Children.

American journal of hematology·2026
Same author

GBSC: Graph-Based Sequence Clustering method for similar short tandem repeats in protein sequences.

Bioinformatics (Oxford, England)·2026
Same author

Giant Benign Ovarian Serous Cystadenoma in a Postmenopausal Woman.

Cureus·2026
Same author

Mapping Current and Emerging Laboratory Techniques for Haemoglobinopathy Carrier Detection and Prevention: A Narrative Review from the HELIOS Action.

International journal of molecular sciences·2026
Same author

Stem cell function <i>in vivo</i> is supported by an alternative glycolysis endpoint.

bioRxiv : the preprint server for biology·2026
Same author

FAIR data gaps and collaboration willingness among hemoglobinopathy research centers.

Scientific data·2026

Related Experiment Video

Updated: May 25, 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

A comparative study on filtering protein secondary structure prediction.

Petros Kountouris1, Michalis Agathocleous, Vasilis J Promponas

  • 1Department of Computer Science, University of Cyprus, 75 Kallipoleos Avenue, PO Box 20537, 1678 Nicosia, Cyprus. kountour@cs.ucy.ac.cy

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 1, 2012
PubMed
Summary
This summary is machine-generated.

Filtering Protein Secondary Structure Prediction (PSSP) improves results and performance. Combining machine learning and empirical rules yielded the greatest improvements in this study.

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: May 25, 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
  • Biophysics
  • Machine learning

Background:

  • Protein secondary structure prediction (PSSP) is crucial for understanding protein function.
  • Existing PSSP methods often produce results lacking physicochemical realism.
  • Filtering PSSP outputs aims to enhance biological plausibility and predictive accuracy.

Purpose of the Study:

  • To compare the effectiveness of different filtering techniques for PSSP.
  • To investigate the impact of machine learning and empirical rules on PSSP filtering.
  • To identify optimal strategies for improving PSSP realism and performance.

Main Methods:

  • Comparative analysis of various filtering approaches for PSSP.
  • Implementation of machine learning algorithms for structure filtering.
  • Application of empirical rules-based methods for PSSP refinement.
  • Evaluation of combined machine learning and empirical rule strategies.

Main Results:

  • Filtering generally enhances the predictive performance of PSSP.
  • Machine learning techniques and empirical rules both contribute to improved PSSP realism.
  • Combinations of machine learning and empirical rules demonstrated the highest level of improvement in PSSP.

Conclusions:

  • Filtering is a valuable step for improving PSSP outputs.
  • Hybrid approaches integrating machine learning and empirical rules offer the most significant advancements in PSSP.
  • Physicochemically realistic PSSP results are achievable through optimized filtering strategies.