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

A knowledge-based forcefield for protein-protein interface design.

Louis A Clark1, Herman W T van Vlijmen

  • 1Biogen Idec Inc., Protein Engineering Group, Cambridge, Massachusetts 02142, USA. louie@alumni.northwestern.edu

Proteins
|October 3, 2007
PubMed
Summary
This summary is machine-generated.

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

Application of AlphaFold models in evaluating ligandable cysteines across E3 ligases.

Proteins·2024
Same author

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning.

Journal of cheminformatics·2023
Same author

Divide and Conquer. Pocket-Opening Mixed-Solvent Simulations in the Perspective of Docking Virtual Screening Applications for Drug Discovery.

Journal of chemical information and modeling·2022
Same author

DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology.

Journal of cheminformatics·2021
Same author

Quantitative prediction of selectivity between the A<sub>1</sub> and A<sub>2A</sub> adenosine receptors.

Journal of cheminformatics·2021
Same author

Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs.

Journal of chemical information and modeling·2020

A new knowledge-based potential accurately predicts protein-protein interactions and orientations using structural data. This method enhances protein design by generating stable, well-oriented complexes with improved binding free energies.

Area of Science:

  • Computational Biology
  • Structural Biology
  • Protein Engineering

Background:

  • Protein-protein interactions are crucial for biological processes.
  • Predicting and designing these interactions remains a significant challenge in structural biology.
  • Existing methods often require extensive computational resources or detailed structural information.

Purpose of the Study:

  • To develop and validate a distance-dependent, knowledge-based potential for protein-protein interactions.
  • To apply this potential to protein design, specifically for interface design and protein-protein docking.
  • To assess the performance of the new potential against established methods like Lennard-Jones potentials.

Main Methods:

  • Extraction of residue-specific C(alpha) and C(beta) pair distances from Protein Data Bank (PDB) crystal structures.

Related Experiment Videos

  • Formulation of radial distribution functions from extracted distance data.
  • Application of the potential for generating protein-protein orientation poses using minimal structural information.
  • Interface design via pose generation and sidechain repacking, followed by localized protein-protein docking tests.
  • Comparison with Lennard-Jones potentials and sophisticated all-atom potentials.

    • Main Results:

    • The knowledge-based potential successfully generated designable poses with low RMSD to known structures for 39 antibody-antigen complexes.
    • 77% of designed complexes exhibited negative free energies of binding, indicating stable interactions.
    • The potential improved localized docking performance compared to non-specific potentials.
    • Larger interface separation generally enhanced designability but reduced binding strength.

    Conclusions:

    • The derived distance-dependent knowledge-based potential is effective for protein design and docking.
    • This approach offers a computationally efficient method for predicting protein-protein orientations and designing stable interfaces.
    • The findings suggest that knowledge-based potentials can significantly improve the accuracy and success rate of protein design strategies.