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 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 and Protein Structures

Protein and Protein Structures

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...

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods·2026

Same author

Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3.

bioRxiv : the preprint server for biology·2026

Same author

ConforNets: Latents-Based Conformational Control in OpenFold3.

ArXiv·2026

Same author

GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model.

bioRxiv : the preprint server for biology·2026

Same author

AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.

Nature methods·2026

Same author

Redefining druggable targets with artificial intelligence.

Nature biotechnology·2025

Search research articles

Related Experiment Video

Updated: Nov 5, 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

Machine learning in protein structure prediction.

Mohammed AlQuraishi¹

¹Program for Mathematical Genomics, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.

Current Opinion in Chemical Biology

|May 20, 2021

Summary

Recent advances in neural networks have revolutionized protein structure prediction, achieving high accuracy. This breakthrough is transforming biomolecular modeling in life sciences.

Keywords:

Alphafold Biophysics Deep learning Machine learning Protein design Protein folding Protein modeling Protein structure Protein structure prediction

More Related Videos

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

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

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

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Nov 5, 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

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

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

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

Published on: January 26, 2024

Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Protein structure prediction from amino acid sequence is a long-standing challenge in molecular biology.
Traditional methods relied on energy models and sampling, with historically variable progress.
Recent years have seen significant advancements driven by artificial intelligence.

Purpose of the Study:

To highlight the transformative impact of neural networks on protein structure prediction.
To detail how neural networks are being integrated into various stages of the prediction pipeline.
To underscore the achievement of unprecedented prediction accuracy.

Main Methods:

Reformulation of contact extraction from evolutionary data using neural networks.
Application of neural networks to distill sequence-structure patterns from known protein data.
Integration of homologous templates from the Protein Data Bank via neural network approaches.
Refinement of coarse structural predictions into high-resolution models using neural networks.

Main Results:

Neural network-based pipelines now predict single protein domains with a median accuracy of 2.1 Å.
This represents a dramatic improvement over previous computational approaches.
The neuralization of structure prediction has led to a paradigm shift in the field.

Conclusions:

The integration of neural networks has dramatically advanced protein structure prediction capabilities.
High-accuracy predictions are now achievable, significantly impacting biomolecular modeling.
This technological leap is poised to reconfigure the role of computational methods in life sciences research.