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

8.3K
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....
8.3K
Protein Organization01:13

Protein Organization

152.5K
Overview
152.5K
Conserved Binding Sites01:49

Conserved Binding Sites

4.8K
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...
4.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.6K
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...
13.6K
Protein and Protein Structure02:15

Protein and Protein Structure

84.5K
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...
84.5K
Protein Families02:47

Protein Families

16.3K
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...
16.3K

You might also read

Related Articles

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

Sort by
Same author

Maintenance Treatment With Gilteritinib Suppresses Post-transplant Relapse in Relapse/Refractory FLT3-Mutated Acute Myeloid Leukemia.

Transplantation and cellular therapy·2026
Same author

Multimodal Artificial Intelligence for Predicting 3- and 5-Year Risks of Myopic Choroidal Neovascularization in High Myopia.

Ophthalmology. Retina·2026
Same author

A start codon-targeted genome editing strategy for generating hypomorphic mutants of lethal plant genes.

Plant biotechnology (Tokyo, Japan)·2026
Same author

Under pressure: integrated endothelial cell response to hydrostatic and shear stresses.

Vascular biology (Bristol, England)·2025
Same author

The consolidation of open-source computer-assisted chemical synthesis data into a comprehensive database.

Journal of cheminformatics·2025
Same author

Ulcerative Colitis after Administration of Dupilumab, an Anti-interleukin-4 Receptor Subunit α Monoclonal Antibody.

Internal medicine (Tokyo, Japan)·2025
Same journal

Editorial: Technologies for RNA Detection.

Bio-protocol·2026
Same journal

One-Step Affinity Purification of MarathonRT Reverse Transcriptase for RNA Sequencing Applications.

Bio-protocol·2026
Same journal

Enhanced RNA-Seq Expression Profiling and Functional Enrichment in Non-model Organisms Using Custom Annotations.

Bio-protocol·2026
Same journal

Using Combined Fluorescent In Situ Hybridization With Immunohistochemistry to Co-localize mRNA in Diverse Neuronal Cell Types.

Bio-protocol·2026
Same journal

Stepwise Protocol for Alternative Splicing Analysis in Single-Cell SMART-Seq2 RNA-Seq Data.

Bio-protocol·2026
Same journal

Enriching Bacteria-Specific RNA From Host Samples Before NGS With Transcript-Capture.

Bio-protocol·2026
See all related articles

Related Experiment Video

Updated: Nov 15, 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

Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction.

Shuichiro Makigaki1, Takashi Ishida1

  • 1School of Computing, Tokyo Institute of Technology, Tokyo, Japan.

Bio-Protocol
|March 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new machine learning method for protein structure prediction. It improves sequence alignment accuracy for more reliable template-based modeling.

Keywords:
Homology modelingMachine learningSequence alignmentTemplate-based modelingk-Nearest Neighbor

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

611
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.8K

Related Experiment Videos

Last Updated: Nov 15, 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

611
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.8K

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in bioinformatics

Background:

  • Template-based modeling predicts protein tertiary structure using homologous proteins.
  • Current methods struggle with accuracy due to suboptimal sequence alignments from homology detection.
  • Improving sequence alignment is crucial for enhancing template-based modeling accuracy.

Purpose of the Study:

  • To develop a novel method for generating accurate pairwise sequence alignments for template-based protein modeling.
  • To enhance the precision of protein structure prediction by optimizing sequence alignment strategies.

Main Methods:

  • A machine learning model is trained using structural alignments of known protein homologs.
  • The method dynamically predicts substitution scores, replacing fixed substitution matrices during sequence alignment.
  • This approach focuses on improving pairwise sequence alignments for template-based modeling.

Main Results:

  • The proposed method generates more accurate sequence alignments compared to traditional approaches.
  • This leads to improved accuracy in template-based protein structure modeling.
  • The dynamic prediction of substitution scores enhances alignment quality.

Conclusions:

  • The novel machine learning approach significantly advances template-based protein modeling.
  • Accurate sequence alignments are key to reliable protein structure prediction.
  • This method offers a more robust solution for predicting protein tertiary structures.