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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.6K
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...
13.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.8K
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...
4.8K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.6K
3.6K

You might also read

Related Articles

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

Sort by
Same author

Paraneoplastic Pemphigus and Liver Biopsy Needle Track Dissemination in Hepatocellular Carcinoma: A Case Report.

GE Portuguese journal of gastroenterology·2026
Same author

Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning.

Nature chemical biology·2025
Same author

A Simple yet Efficient Water-Saving Condenser.

ACS omega·2025
Same author

Opportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions.

Journal of chemical theory and computation·2025
Same author

Computationally designed stem-epitope mimetics elicit broadly reactive antibodies.

bioRxiv : the preprint server for biology·2025
Same author

Influenza A(H1N1)pdm09 virus resistance to baloxavir, oseltamivir and sialic acid mimetics in single and dual therapies: Insights from human airway epithelia and murine models.

Antiviral research·2025

Related Experiment Video

Updated: Nov 16, 2025

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

17.3K

A Rosetta-based protein design protocol converging to natural sequences.

Giulia Sormani1, Zander Harteveld2, Stéphane Rosset2

  • 1SISSA, Via Bonomea 265, Trieste, Italy.

The Journal of Chemical Physics
|February 20, 2021
PubMed
Summary
This summary is machine-generated.

Computational protein design using Rosetta can generate novel sequences, but they may lack natural signatures. A new genetic algorithm approach improves sequence recognition and experimental protein stability.

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K
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

614

Related Experiment Videos

Last Updated: Nov 16, 2025

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

17.3K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K
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

614

Area of Science:

  • Protein Engineering
  • Computational Biology
  • Bioinformatics

Background:

  • Computational protein design aims to identify sequences for specific structures.
  • Rosetta suite protocols are widely used but have limitations.
  • Understanding Rosetta's strengths and weaknesses is crucial for protein engineering.

Purpose of the Study:

  • Evaluate Rosetta design protocols on SH3-1 and Ubiquitin folds.
  • Identify limitations in Rosetta's sequence optimization and scoring functions.
  • Develop an improved protocol for generating natural-like protein sequences.

Main Methods:

  • Tested Rosetta design protocols on native and polyvaline starting sequences.
  • Utilized BLAST and Hmmer for sequence recognition analysis.
  • Developed a genetic algorithm protocol embedding Rosetta Design.

Main Results:

  • Rosetta converged to similar sequences regardless of starting conditions, lacking natural signatures.
  • Generated sequences were not recognized by standard bioinformatics tools.
  • The new genetic algorithm protocol produced sequences with recognizable natural signatures.

Conclusions:

  • Rosetta's scoring function exhibits inaccuracies, drifting towards non-natural sequences.
  • The developed genetic algorithm protocol enhances sequence naturalness and experimental stability.
  • This approach improves the reliability of computational protein design for engineering functional proteins.