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 Experiment Videos

Structure-based method for analyzing protein-protein interfaces.

Ying Gao1, Renxiao Wang, Luhua Lai

  • 1State Key Laboratory of Structural Chemistry for Stable and Unstable Species, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China.

Journal of Molecular Modeling
|November 25, 2003
PubMed
Summary

This study introduces a new method to analyze protein-protein interactions using non-covalent forces. It accurately predicts hot-spot residues, crucial for binding affinity, even with conformational changes.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

PSFF-PTM: A Coarse-Grained Force-Field Parameter Patch for Modeling Post-Translational Modification Effects on Biomolecular Condensates.

Journal of chemical theory and computation·2026
Same author

Design, Synthesis, and Evaluation of Phenylpyrrole Derivatives as Small-Molecule Activators of BAX.

Journal of medicinal chemistry·2026
Same author

Combined Nudged Elastic Band and Markov State Models Uncover Distinct BAX Activation Pathways by Bim Peptide and a Small Molecule.

JACS Au·2026
Same author

COMET: A Machine-Learning Framework Integrating Ligand-Based and Target-Based Algorithms for Elucidating Drug Targets.

Journal of medicinal chemistry·2025
Same author

Bioorthogonal Photocatalytic Protein Labeling and Cross-Linking Enabled by Stabilized Ketyl Radicals.

Journal of the American Chemical Society·2025
Same author

MGTbind: a comprehensive database of molecular glue ternary interactome.

Nucleic acids research·2025

Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Structural Biology

Background:

  • Protein-protein interactions (PPIs) are fundamental to cellular processes.
  • Non-covalent interactions, including hydrogen bonds, hydrophobic, and van der Waals forces, govern PPIs.
  • Understanding residue contributions to binding affinity is critical for drug design and biological research.

Purpose of the Study:

  • To develop and validate a computational method for analyzing PPIs based on non-covalent interactions.
  • To quantitatively estimate the contribution of individual interfacial residues to protein binding.
  • To assess the method's accuracy in predicting hot-spot residues and its robustness to conformational changes.

Main Methods:

  • Development of a novel computational approach utilizing hydrogen bond, hydrophobic, and van der Waals interactions.

Related Experiment Videos

  • Application of the method to alanine mutation data from 13 protein-protein complexes (250 mutations).
  • Validation using a dataset of 26 complexes with unbound components to assess conformational change tolerance.

    • Main Results:

    • The method correctly predicted 66 out of 75 hot-spot residues (88% success rate) in alanine scanning mutagenesis studies.
    • The method demonstrated robustness to conformational changes, with only an 11% difference in key residue identification between complexed and unbound forms.
    • The approach provides a graphic display of residue contributions and binding partner characteristics.

    Conclusions:

    • The developed method effectively analyzes protein-protein interactions and predicts critical binding residues.
    • The approach is reliable across different conformational states, offering insights into protein recognition.
    • This tool facilitates in-depth studies of PPIs, mutation effects, and correlated mutations.