Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Organization

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 Organization

Protein Organization

Protein Folding

Protein Folding

Protein Folding

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 and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

Protein Folding Quality Check in the RER

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...

You might also read

Related Articles

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

Sort by

Same author

Small-molecule binding and sensing with a designed protein family.

Nature communications·2026

Same author

LetA defines a structurally distinct transporter family.

Nature·2026

Same author

Modeling protein-small molecule conformational ensembles with PLACER.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Predicting protein-protein interactions in the human proteome.

Science (New York, N.Y.)·2025

Same author

Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm.

Nature communications·2025

Same author

Computational design of serine hydrolases.

Science (New York, N.Y.)·2025

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

Ivan Anishchenko¹, Minkyung Baek¹, Hahnbeom Park¹

¹Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington, USA.

|July 31, 2021

Summary

The enhanced trRosetta method uses deep learning with language model embeddings and template information for faster, more accurate protein structure prediction. This improved pipeline significantly outperforms the original trRosetta, requiring fewer computational resources.

Keywords:

Rosetta deep learning metagenomes protein structure prediction refinement

More Related Videos

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

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

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: Oct 26, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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

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

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 bioinformatics
Deep learning applications in protein science

Background:

The trRosetta method predicts protein structures using deep learning to model residue-residue distances and orientations.
Accurate protein structure prediction is crucial for understanding biological function and disease mechanisms.

Purpose of the Study:

To enhance the trRosetta method for improved protein structure prediction accuracy and efficiency.
To integrate language model embeddings and template information into the deep learning pipeline.
To develop a refinement strategy combining template-free and template-based models guided by an accuracy predictor.

Main Methods:

Incorporated language model embeddings and template information, weighted by sequence similarity, as inputs to the deep learning model.
Developed a refinement pipeline utilizing the DeepAccNet accuracy predictor to guide model recombination.
Augmented input data by identifying homologous sequences for network input.

Main Results:

The enhanced trRosetta pipeline demonstrated considerable improvement over the original method in both benchmark tests and CASP14.
The improved method is faster and requires fewer computational resources, completing modeling in under 3 hours.
Human group performance was further enhanced by identifying additional homologous sequences.

Conclusions:

The enhanced trRosetta pipeline represents a significant advancement in protein structure prediction.
The integration of new inputs and refinement strategy leads to higher accuracy and efficiency.
Further improvements may be achieved by addressing challenges like missing inter-domain or inter-chain contacts.