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-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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,...
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,...
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.
Conserved Binding Sites01:49

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 analyses the...

You might also read

Related Articles

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

Sort by
Same author

Automated biomedical hypothesis generation with time-aware hypergraph contrastive learning.

Knowledge and information systems·2026
Same author

Cell-o1 : training LLMs to solve single-cell reasoning puzzles with reinforcement learning.

Bioinformatics (Oxford, England)·2026
Same author

β-Substitution and prodrug derivation leading to identification of fosmidomycin analogs with improved herbicidal activity.

Pest management science·2026
Same author

Genome-Wide Characterization of the <i>Expansin</i> Gene Family in Eggplant (<i>Solanum melongena</i> L.) Reveals Its Roles in Fruit Development and Heat Stress Response.

Plants (Basel, Switzerland)·2026
Same author

Integrating Social Determinants of Health in a Multi-Modal Deep Clustering Survival Model for Injury-Risk in Alzheimer's and Related Dementia Patients.

Proceedings of machine learning research·2026
Same author

Superior In Vivo Efficacy for T Cell Engineering via Citronellol-Tailored mRNA-tLNPs.

Small (Weinheim an der Bergstrasse, Germany)·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

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge.

Young-Rae Cho1, Lei Shi, Murali Ramanathan

  • 1Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu

BMC Bioinformatics
|September 20, 2008
PubMed
Summary
This summary is machine-generated.

Predicting protein functions is challenging due to inaccurate interaction data. This study presents a probabilistic framework using functional similarity to improve protein function prediction, outperforming existing methods.

More Related Videos

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

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

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

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Functional characterization of novel proteins is a key challenge in the post-genomic era.
  • Protein-protein interactions (PPIs) offer functional insights but suffer from experimental noise and lack of functional linkage.
  • High-throughput methods generate extensive PPI data, yet accuracy remains a limitation for functional analysis.

Purpose of the Study:

  • To develop a probabilistic framework for predicting functions of unknown proteins.
  • To leverage functional similarity between interacting proteins for improved prediction accuracy.
  • To address the challenge of false positives in experimentally derived PPI data.

Main Methods:

  • Integration of PPI data with functional knowledge from the Gene Ontology (GO) database.
  • Application of similarity measures to quantify functional similarity between interacting proteins.
  • Development and validation of a probabilistic model for function prediction using leave-one-out cross-validation.

Main Results:

  • The proposed algorithm demonstrates superior prediction accuracy compared to existing methods.
  • The framework effectively handles high false positive rates inherent in current PPI data.
  • Functional similarity assessment enhances the reliability of protein function prediction.

Conclusions:

  • Experimentally determined PPIs alone are often insufficient for accurate functional association discovery.
  • Integrating multiple data sources, including functional similarity, significantly enhances the prediction performance for uncharacterized proteins.
  • The developed probabilistic approach offers a robust solution for functional genomics challenges.