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

Valence Bond Theory02:45

Valence Bond Theory

34.6K
Overview of Valence Bond Theory
34.6K
Bond Energies and Bond Lengths02:49

Bond Energies and Bond Lengths

26.1K
Stable molecules exist because covalent bonds hold the atoms together. The strength of a covalent bond is measured by the energy required to break it, that is, the energy necessary to separate the bonded atoms. Separating any pair of bonded atoms requires energy — the stronger a bond, the greater the energy required to break it.
26.1K
Bond Polarity, Dipole Moment, and Percent Ionic Character02:48

Bond Polarity, Dipole Moment, and Percent Ionic Character

30.5K
Bond Polarity
30.5K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

20.1K
Molecular Orbital Energy Diagrams
20.1K
Chemical Bonds02:40

Chemical Bonds

18.8K

Atoms participate in a chemical bond formation to acquire a completed valence-shell electron configuration similar to that of the noble gas nearest to it in atomic number. Ionic, covalent, and metallic bonds are some of the important types of chemical bonds. Bond energy and bond length determine the strength of a chemical bond.
Types of Chemical Bonds
An ionic bond is formed due to electrostatic attraction between cations and anions. Often, the ions are formed by the transfer of electrons...
18.8K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

11.6K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Automatic Explanation of Protein-Protein Binding Mechanism: A Preliminary Study.

Computational structural bioinformatics : international workshop, CSBW 2024, Boston, MA, USA, November 16, 2024, proceeding. Computational Structural Bioinformatics Workshop (2024 : Boston, Mass.)·2026
Same author

A Conical Representation of Hydrogen Bond Geometry for Quantifying Bond Interactions.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2025
Same author

Abnormal response to chronic social defeat stress and fear extinction in a mouse model of <i>Lynx</i>2-based cholinergic dysregulation.

Frontiers in neuroscience·2025
Same author

A novel anxiety-associated SNP identified in <i>LYNX2</i> (<i>LYPD1</i>) is associated with decreased protein binding to nicotinic acetylcholine receptors.

Frontiers in behavioral neuroscience·2025
Same author

Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

IEEE transactions on medical imaging·2024
Same author

Lynx1 and the family of endogenous mammalian neurotoxin-like proteins and their roles in modulating nAChR function.

Pharmacological research·2023
Same journal

A Knowledge-Guided Large Language Model Framework for Microbiome-Based Disease Diagnosis.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
Same journal

Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
Same journal

Geospatial Analysis of Socioeconomic Equity and Environmental Factors Influencing Lung Cancer Prevalence in US.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
Same journal

Modeling TCR-pMHC Binding with Dual Encoders and Cross-Attention Fusion.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
Same journal

Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
Same journal

Uncovering the Role of Neuropsychiatric Symptoms in Cognitive Impairment Progression.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2026
See all related articles

Related Experiment Video

Updated: Sep 28, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.6K

DiffBond: A Method for Predicting Intermolecular Bond Formation.

Justin Tam1, Talulla Palumbo2, Julie M Miwa2

  • 1Dept. Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|April 5, 2022
PubMed
Summary
This summary is machine-generated.

DiffBond is a new computational method that identifies hydrogen bonds, ionic bonds, and salt bridges in protein complexes. This tool precisely characterizes intermolecular interactions, aiding in the prediction of key amino acids.

More Related Videos

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
06:44

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

Published on: March 24, 2018

69.2K
Molecular Spring Constant Analysis by Biomembrane Force Probe Spectroscopy
08:10

Molecular Spring Constant Analysis by Biomembrane Force Probe Spectroscopy

Published on: November 20, 2021

3.1K

Related Experiment Videos

Last Updated: Sep 28, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.6K
From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
06:44

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

Published on: March 24, 2018

69.2K
Molecular Spring Constant Analysis by Biomembrane Force Probe Spectroscopy
08:10

Molecular Spring Constant Analysis by Biomembrane Force Probe Spectroscopy

Published on: November 20, 2021

3.1K

Area of Science:

  • Computational biology
  • Structural biology
  • Biochemistry

Background:

  • Protein-protein interactions are crucial for biological processes.
  • Analyzing intermolecular interactions provides insights into protein recognition.
  • Existing computational methods may not fully differentiate specific interaction types.

Purpose of the Study:

  • To introduce DiffBond, a novel computational method for analyzing intermolecular interactions in protein complexes.
  • To differentiate between hydrogen bonds, ionic bonds, and salt bridges.
  • To assess the precision and recall of DiffBond in identifying these interactions.

Main Methods:

  • Development of the DiffBond algorithm incorporating definitions of hydrogen bonds, ionic bonds, and salt bridges.
  • Application of DiffBond to analyze known protein complexes (barnase-barstar, Rap1a-raf, Smad2-Smad4, and toxin-nAChR complexes).
  • Evaluation of DiffBond's performance using precision and recall metrics.

Main Results:

  • DiffBond accurately identified ionic and hydrogen bonds with high precision and recall.
  • DiffBond demonstrated high precision in identifying salt bridges.
  • The method effectively handles uncertainties inherent in computational protein models.

Conclusions:

  • DiffBond is a precise and effective tool for characterizing intermolecular interactions in protein complexes.
  • It aids in predicting influential amino acids involved in protein-protein recognition.
  • Combines well with other electrostatic analysis methods for comprehensive interaction studies.