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

12.5K
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...
12.5K
Protein Networks02:26

Protein Networks

3.7K
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,...
3.7K
Protein Networks02:26

Protein Networks

1.8K
1.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.4K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.4K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712

You might also read

Related Articles

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

Sort by
Same author

Enhancing biomedical search interfaces with images.

Bioinformatics advances·2023
Same author

Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities.

Journal of the American Medical Informatics Association : JAMIA·2022
Same author

Domain-Informed Neural Networks for Interaction Localization Within Astroparticle Experiments.

Frontiers in artificial intelligence·2022
Same author

Translational drug-interaction corpus.

Database : the journal of biological databases and curation·2022
Same author

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes.

Computational and structural biotechnology journal·2021
Same author

Corrigendum to: Utilizing image and caption information for biomedical document classification.

Bioinformatics (Oxford, England)·2021
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: May 2, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

Ramanuja Simha, Hagit Shatkay1

  • 1Department of Computer and Information Sciences, University of Delaware, Newark DE, USA. shatkay@udel.edu.

Algorithms for Molecular Biology : AMB
|March 21, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method to predict protein subcellular locations, improving accuracy by considering inter-dependencies between locations. The approach enhances the understanding of protein function and potential drug targets.

More Related Videos

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

9.6K
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

1.5K

Related Experiment Videos

Last Updated: May 2, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K
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

9.6K
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

1.5K

Area of Science:

  • Computational Biology
  • Proteomics
  • Bioinformatics

Background:

  • Accurate prediction of protein subcellular localization is crucial for understanding protein function, biological processes, and identifying drug targets.
  • Existing computational methods often assume single protein localization, neglecting the reality of multi-localization and its implications.
  • Current multi-localization prediction systems have limitations and fail to leverage potential inter-dependencies among cellular locations.

Purpose of the Study:

  • To develop a novel computational method that explicitly incorporates inter-dependencies among protein subcellular locations.
  • To improve the accuracy of predicting multi-localized proteins by integrating location relationships into the prediction model.
  • To advance the field of protein localization prediction beyond single-location assumptions.

Main Methods:

  • Developed a system based on a collection of Bayesian network classifiers, where each classifier predicts a single location.
  • Incorporated inter-dependencies among locations during the learning phase of each Bayesian network classifier.
  • Utilized multi-location estimates in the prediction process, leveraging learned location relationships.

Main Results:

  • The developed method significantly improved prediction performance by incorporating inter-dependencies among locations compared to methods that do not.
  • Evaluated on a comprehensive dataset of single- and multi-localized proteins (derived from DBMLoc).
  • Achieved performance comparable to a top system (YLoc+) for multi-localized proteins, without being restricted to training set combinations.

Conclusions:

  • Directly incorporating inter-dependencies among protein subcellular locations enhances prediction accuracy for multi-localized proteins.
  • The proposed Bayesian network approach offers a promising direction for improving computational protein localization prediction.
  • This method provides a more realistic and accurate prediction of protein localization, aiding functional and drug discovery research.