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

Protein Folding01:22

Protein Folding

121.9K
Overview
121.9K
Protein Organization01:24

Protein Organization

7.3K
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....
7.3K
Protein and Protein Structure02:15

Protein and Protein Structure

81.7K
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...
81.7K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.5K
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...
11.5K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

2.4K
2.4K

You might also read

Related Articles

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

Sort by
Same author

The NMR Exchange Format (NEF): Specification and Applications.

bioRxiv : the preprint server for biology·2026
Same author

MolViewStories: Interactive molecular storytelling.

Protein science : a publication of the Protein Society·2026
Same author

RCSB Protein Data Bank: Delivering integrative structures alongside experimental structures and computed structure models.

Nucleic acids research·2025
Same author

MolViewSpec: a Mol* extension for describing and sharing molecular visualizations.

Nucleic acids research·2025
Same author

Where and how to house big data on small fragments.

Nature communications·2025
Same author

A new chapter for RCSB Protein Data Bank Molecule of the Month in 2025.

Structural dynamics (Melville, N.Y.)·2025
Same journal

Report of high data rate macromolecular crystallography (HDRMX) meeting, 23 July 2025.

Structural dynamics (Melville, N.Y.)·2026
Same journal

Directional sensitivity of the <math><mrow><mrow><msub><mrow><mi>A</mi></mrow> <mrow><mn>1</mn> <mi>g</mi></mrow></msub></mrow></mrow></math> phonon in biaxially strained bismuth heterofilms studied by transient white light reflectivity.

Structural dynamics (Melville, N.Y.)·2026
Same journal

Erratum: "First experiments with ultrashort, circularly polarized soft x-ray pulses at FLASH2" [Struct. Dyn. <b>12</b>, 034301 (2025)].

Structural dynamics (Melville, N.Y.)·2026
Same journal

<sup>13</sup>C NMR as a foundation for machine learning models of polysaccharides.

Structural dynamics (Melville, N.Y.)·2026
Same journal

Bromodomain dimers: A case study of BRD4 and family-wide AlphaFold predictions.

Structural dynamics (Melville, N.Y.)·2026
Same journal

Integrating metabolomics and histopathology: A method for metabolite recovery from fixed tissue specimens.

Structural dynamics (Melville, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

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

Published on: July 16, 2017

15.6K

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning.

Alexander M Ille1, Emily Anas2, Michael B Mathews

  • 1Rutgers Cancer Institute, Rutgers, The State University of New Jersey, Newark, New Jersey 07103, USA.

Structural Dynamics (Melville, N.Y.)
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence/machine learning models like AlphaFold2 predict protein structures from amino acid sequences. Future AI/ML models may predict protein conformational dynamics using sequence and nuclear magnetic resonance data.

More Related Videos

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

383
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Related Experiment Videos

Last Updated: Sep 18, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

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

Published on: July 16, 2017

15.6K
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

383
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Area of Science:

  • Biochemistry and Structural Biology
  • Computational Biology and Bioinformatics
  • Artificial Intelligence in Life Sciences

Background:

  • The 2024 Nobel Prize in Chemistry recognized AI/ML models for *de novo* protein structure prediction.
  • Models like AlphaFold2, RoseTTAFold, and ESMFold utilize neural networks and attention mechanisms.
  • These AI/ML models are based on the hypothesis that protein structure is determined by its amino acid sequence.

Purpose of the Study:

  • To review the sequence-structure relationship in proteins.
  • To propose that protein conformational dynamics are also sequence-dependent.
  • To outline a conceptual AI/ML model for predicting protein conformational ensembles.

Main Methods:

  • Overview of existing AI/ML models for protein structure prediction (AlphaFold2, RoseTTAFold, ESMFold).
  • Discussion of the underlying hypothesis linking amino acid sequence to protein structure.
  • Conceptualization of a novel AI/ML model architecture for conformational dynamics prediction.

Main Results:

  • AI/ML models have demonstrated success in predicting static protein structures.
  • AlphaFold2 can predict multiple protein conformations by subsampling sequence alignments.
  • Nuclear magnetic resonance (NMR) spectroscopy provides conformationally sensitive data suitable for AI/ML training.

Conclusions:

  • The deterministic relationship between protein sequence and structure is well-established.
  • Protein conformational dynamics are likely sequence-dependent, offering new avenues for AI/ML.
  • Sequence-informed prediction of protein dynamics using AI/ML and NMR data holds transformative potential for biological sciences.