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 Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
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.
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...

You might also read

Related Articles

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

Sort by
Same author

Assessment of Macular Thickness in Myopic Patients and Its Correlation with Axial Length: A Hospital-Based Study.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)·2026
Same author

Phosphorylation of Runx protein controls helper CD4<sup>+</sup> T cell versus cytotoxic CD8<sup>+</sup> T cell lineage choice.

Nature immunology·2026
Same author

Uncertainty-Aware Learning of Multiple Conditions as a Framework for Streamlined Retention Time Prediction to Accelerate Method Development.

Analytical chemistry·2026
Same author

Discovery of novel 4-aminobenzamide derivatives as small molecule CBX2 inhibitors.

Bioorganic & medicinal chemistry letters·2026
Same author

GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.

ACS medicinal chemistry letters·2025
Same author

The Ser7 phosphorylation of RNA polymerase II-CTD is required for the recruitment of E3 ubiquitin ligase Asr1 and subtelomeric gene silencing.

The Journal of biological chemistry·2025
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 20, 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 probabilistic fragment-based protein structure prediction algorithm.

David Simoncini1, Francois Berenger, Rojan Shrestha

  • 1Zhang Initiative Research Unit, Advanced Science Institute, RIKEN, Wako, Saitama, Japan.

Plos One
|July 26, 2012
PubMed
Summary
This summary is machine-generated.

EdaFold, a new protein structure prediction method, uses an Estimation of Distribution Algorithm to improve conformational sampling, generating more accurate models for molecular replacement. This approach enhances near-native decoy generation, aiding in solving the crystallographic phase problem.

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

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 20, 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

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
  • Structural Biology
  • Bioinformatics

Background:

  • Fragment-based protein structure prediction methods face challenges in conformational sampling.
  • Current methods often use uniform probability distributions for fragment selection, limiting exploration of native-like regions.

Purpose of the Study:

  • To introduce EdaFold, a novel fragment-based protein structure prediction method.
  • To enhance the sampling of native-like conformational space in protein structure prediction.
  • To improve the accuracy of all-atom protein models generated from coarse-grained decoys.

Main Methods:

  • Developed EdaFold, employing an Estimation of Distribution Algorithm (EDA).
  • EDA learns from generated decoys to guide the search towards native-like conformations.
  • Compared EdaFold's performance against the Rosetta AbInitio protocol on a protein benchmark.

Main Results:

  • EdaFold generated protein models with lower energies compared to Rosetta.
  • EdaFold significantly increased the percentage of near-native coarse-grained decoys.
  • All-atom models derived from EdaFold decoys showed higher success rates in molecular replacement.

Conclusions:

  • Improving coarse-grained decoy accuracy enhances subsequent all-atom refinement.
  • EdaFold effectively addresses conformational sampling bottlenecks in fragment-based prediction.
  • EdaFold facilitates the generation of accurate all-atom models for solving the crystallographic phase problem.