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

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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...

Conservation of Protein Domains

Conservation of Protein Domains

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 Folding

Protein Folding

Protein Folding

Protein Folding

Conserved Binding Sites

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...

You might also read

Related Articles

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

Sort by

Same author

Accelerated Sampling of Protein Dynamics Using BioEmu-Augmented Molecular Simulation.

Journal of chemical information and modeling·2026

Same author

An intranasal adenoviral-vectored vaccine protects against highly pathogenic avian influenza H5N1 in naive and antigen-experienced animals.

Cell reports. Medicine·2026

Same author

Structural and kinetic characterization of DUSP5 with a Di-phosphorylated tripeptide substrate from the ERK activation loop.

Frontiers in chemical biology·2025

Same author

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins.

Frontiers in immunology·2024

Same author

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins.

bioRxiv : the preprint server for biology·2024

Same author

A general computational design strategy for stabilizing viral class I fusion proteins.

Nature communications·2024

Search research articles

Related Experiment Video

Updated: Apr 10, 2026

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

ConforFold recovers alternative protein conformations beyond MSA subsampling.

Raulia Syrlybaeva¹, Eva-Maria Strauch^1,2,3

¹Department of Medicine, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.

Protein Science : a Publication of the Protein Society

|April 9, 2026

Summary

This study introduces ConforPSSP and ConforFold, a new framework for protein structure prediction. It accurately recovers multiple protein conformations by integrating secondary structure information, improving upon existing methods.

Keywords:

AlphaFold OpenFold machine learning protein conformers protein secondary structures

More Related Videos

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

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

Published on: July 16, 2017

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Related Experiment Videos

Last Updated: Apr 10, 2026

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

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

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

Published on: July 16, 2017

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein conformational changes are crucial for function, but current prediction tools struggle to model diverse structural ensembles.
Systematic sampling of protein structural ensembles remains a significant challenge in computational biology.

Purpose of the Study:

To develop a novel computational framework for predicting multiple protein conformational states.
To improve the recovery of alternative protein conformations using deep learning and secondary structure information.

Main Methods:

ConforPSSP generates diverse 8-state protein secondary structure predictions (PSSPs) using a transformer model.
ConforFold, a retrained OpenFold model, uses PSSPs to condition structure prediction for conformer recovery.
The framework integrates secondary-structure sampling into deep learning for enhanced ensemble prediction.

Main Results:

ConforFold achieved state-of-the-art performance in recovering protein conformers, identifying both states in 84% of test cases (TM-scores ≥0.8).
Outperformed diffusion-based (AlphaFlow) and MSA clustering (Cfold) methods in conformer recovery.
Demonstrated superior performance over BioEmu in cases with significant secondary structure differences between conformers (83% vs. 76%).

Conclusions:

ConforFold provides a broadly applicable framework for modeling protein structural ensembles.
Explicitly integrating secondary structure allows recovery of conformations missed by other methods.
Offers a new approach for studying protein conformational heterogeneity and function.