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

Conservation of Protein Domains Over Different Proteins02:26

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 form...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
Protein Organization01:24

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.
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Protein and Protein Structure02:15

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 can...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by
Same author

Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells.

bioRxiv : the preprint server for biology·2026
Same author

<i>SPP1</i><sup>hi</sup> macrophages in fibrin niches promote hyperplastic tissue remodeling in rheumatoid arthritis synovium.

Science translational medicine·2026
Same author

Interleukin-12 induces rapid STAT4/DDX5-dependent remodeling of RNA polymerase II occupancy in NK cells.

The Journal of experimental medicine·2026
Same author

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species.

Nature methods·2026
Same author

Label-Free Quantification in the Crux Toolkit.

Journal of proteome research·2026
Same author

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

bioRxiv : the preprint server for biology·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2026

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

Combining classifiers for improved classification of proteins from sequence or structure.

Iain Melvin1, Jason Weston, Christina S Leslie

  • 1NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com

BMC Bioinformatics
|September 24, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning method for protein classification, combining nearest neighbor and support vector machine (SVM) approaches. The novel hybrid classifier achieves full coverage and improved accuracy for predicting protein structures and functions.

More Related Videos

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Jun 30, 2026

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

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning

Background:

  • Protein classification from sequence or structure is a key challenge.
  • Support vector machines (SVMs) show promise but suffer from limited coverage due to data requirements.
  • Existing methods struggle to achieve both high accuracy and broad coverage.

Purpose of the Study:

  • To develop a hybrid machine learning approach for protein classification.
  • To improve the accuracy and coverage of protein classification methods.
  • To assign proteins to structural categories using sequence or 3D structure data.

Main Methods:

  • A hybrid approach combining nearest neighbor and multiclass SVMs.
  • Utilizing a learned threshold to transition between methods.
  • Applying the method to protein sequence and 3D structure data.

Main Results:

  • The hybrid method achieves full coverage with improved accuracy compared to individual methods.
  • Demonstrated superior performance across different coverage levels in cross-validated experiments.
  • Successfully assigned proteins to structural categories within the SCOP hierarchy.

Conclusions:

  • The hybrid machine learning approach offers enhanced protein classification capabilities.
  • This method overcomes the coverage limitations of traditional SVM-based techniques.
  • Code and datasets are publicly available for reproducibility and further research.