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 Organization01:24

Protein Organization

8.2K
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....
8.2K
Protein Organization01:13

Protein Organization

151.6K
Overview
151.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.5K
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...
13.5K
Protein and Protein Structure02:15

Protein and Protein Structure

84.2K
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...
84.2K
Protein and Protein Structures02:15

Protein and Protein Structures

15.3K
15.3K
Protein Families02:47

Protein Families

16.2K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Structural insights into insect-selective sodium channel toxins drive AI-enhanced biopesticide design.

Nature communications·2026
Same author

Integration of pre-trained protein language models into geometric deep learning networks.

Communications biology·2023
Same author

The landscape of tolerated genetic variation in humans and primates.

Science (New York, N.Y.)·2023
Same author

An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Improving protein structure prediction using templates and sequence embedding.

Bioinformatics (Oxford, England)·2022
Same author

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.

PLoS computational biology·2022
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Nov 7, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.3K

Deep template-based protein structure prediction.

Fandi Wu1,2,3, Jinbo Xu1

  • 1Toyota Technological Institute at Chicago, Chicago, IL, United States of America.

Plos Computational Biology
|May 3, 2021
PubMed
Summary
This summary is machine-generated.

NDThreader, a new deep learning method, enhances template-based protein modeling (TBM) accuracy. It outperforms existing methods, achieving top results in CASP14 for protein structure prediction when similar templates are unavailable.

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

542
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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

Published on: July 25, 2013

17.2K

Related Experiment Videos

Last Updated: Nov 7, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.3K
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

542
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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

Published on: July 25, 2013

17.2K

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Deep learning applications

Background:

  • Deep learning has advanced protein structure prediction, primarily in template-free modeling.
  • Template-based modeling (TBM) remains crucial but struggles with accuracy when similar templates are absent.
  • Existing TBM methods require improvement, especially for proteins lacking close template matches.

Purpose of the Study:

  • To develop a novel deep learning method, NDThreader, to enhance template-based protein structure prediction.
  • To address the limitations of current TBM techniques, particularly in scenarios with low template similarity.
  • To improve the accuracy and reliability of 3D protein model construction using TBM.

Main Methods:

  • NDThreader integrates deep convolutional residual neural fields (DRNF) with ResNet and CRF for initial template alignment without distance information.
  • Alternating direction method of multipliers (ADMM) and DRNF are used to refine sequence-template alignments utilizing predicted distance potentials.
  • A deep ResNet predicts inter-atom distance distribution from sequence-template alignments and coevolution data, feeding into PyRosetta for 3D model construction.

Main Results:

  • NDThreader significantly outperforms established methods like CNFpred, HHpred, DeepThreader, and CEthreader.
  • In CASP14, NDThreader, as part of the RaptorX server, achieved the highest average GDT score for TBM targets.
  • The method demonstrates superior performance in accurately predicting protein structures even with limited template similarity.

Conclusions:

  • NDThreader represents a significant advancement in template-based protein structure prediction using deep learning.
  • The method effectively overcomes challenges associated with low template similarity in TBM.
  • NDThreader provides a more accurate and robust approach to 3D protein model building.