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 Folding

Protein Folding

Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

Molecular Chaperones and Protein Folding

Molecular Chaperones and Protein Folding

The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...

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 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...

You might also read

Related Articles

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

Sort by

Same author

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding.

Journal of chemical theory and computation·2026

Same author

Energetics of Noncovalent Interactions of Protein-Ligand Complexes for Drug Discovery.

Journal of chemical information and modeling·2026

Same author

Sensing the acidity of hydrogen bond networks.

Physical chemistry chemical physics : PCCP·2026

Same author

A functional map of the human intrinsically disordered proteome.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

SmileyLlama: modifying large language models for directed chemical space exploration.

Nature computational science·2026

Same author

Conformational Ensembles of the Disordered 4E-BP2:eIF4E Complex Restrained by smFRET Experiments.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.

Oufan Zhang¹, Mojtaba Haghighatlari¹, Jie Li¹

¹Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, California 94720, USA.

The Journal of Chemical Physics

|May 5, 2023

Summary

We developed DynamICE, a Generative Recurrent Neural Network (GRNN), to model disordered protein structures. This computational method uses experimental data to refine protein conformations, improving structural characterization.

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

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Related Experiment Videos

Last Updated: Jul 31, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

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

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

Area of Science:

Computational Biology
Structural Biology
Biophysics

Background:

Characterizing intrinsically disordered proteins (IDPs) requires modeling their dynamic structural ensembles.
Current computational methods for IDPs are limited by conformational sampling and reliance on initial conformer pools.
Experimental data integration is crucial for accurate structural ensemble determination.

Purpose of the Study:

To develop a novel computational approach for modeling disordered protein structures.
To overcome limitations in conformational sampling for IDPs.
To integrate diverse experimental data for enhanced structural characterization.

Main Methods:

Developed a Generative Recurrent Neural Network (GRNN) named DynamICE.
Employed supervised learning to bias torsion probability distributions.
Utilized experimental data (NMR J-couplings, NOEs, PREs) for model training and refinement.
Implemented a reward feedback mechanism based on experimental data agreement.

Main Results:

DynamICE effectively biases torsion distributions using experimental data.
The GRNN learns to physically alter protein conformations, not just reweight existing ones.
Achieved improved agreement between computational models and experimental observations for disordered proteins.
Demonstrated an alternative to traditional reweighting methods for IDP structural ensembles.

Conclusions:

DynamICE offers a novel, data-driven approach for modeling disordered protein structures.
This method enhances the accuracy of structural ensembles by dynamically refining conformations.
The GRNN-based strategy provides a powerful alternative for studying the dynamics of IDPs.