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

Native atom types for knowledge-based potentials: application to binding energy prediction.

Brian N Dominy1, Eugene I Shakhnovich

  • 1Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.

Journal of Medicinal Chemistry
|August 20, 2004
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

Complete enzyme clustering enhances coenzyme Q biosynthesis via substrate channeling.

Nature communications·2026
Same author

Biophysical fitness landscape design traps viral evolution.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chaperonin recognition of protein dynamics drives drug resistance.

bioRxiv : the preprint server for biology·2026
Same author

Co-targeting Metabolic Neighbours Constraints Bacterial Adaptive Evolution.

bioRxiv : the preprint server for biology·2026
Same author

CASPULE: A computational tool to study sticker spacer polymer condensates.

PLoS computational biology·2026
Same author

Evolutionary dynamics under phenotypic uncertainty.

bioRxiv : the preprint server for biology·2026
Same journal

Discovery of Effective Dual PROTAC Degraders with Synergistic Antitumor Activity for Overcoming Tamoxifen-Resistant Breast Cancer.

Journal of medicinal chemistry·2026
Same journal

In Silico ADMET: From Current Practices to Novel Profilers.

Journal of medicinal chemistry·2026
Same journal

A Zirconium Aza-BODIPY Derivative as a Near-Infrared Fluorescent/Photoacoustic Imaging Bimodal Probe.

Journal of medicinal chemistry·2026
Same journal

Landscaping the Nitazene Scaffold to Identify Novel Mu-Opioid Receptor Modulators: From Molecular Design and Chemical Synthesis to Pharmacological Profiling.

Journal of medicinal chemistry·2026
Same journal

Development of Indole-3-yl-methylene-thiobarbital Derivatives as Inhibitors of HDAC8 Enzyme Activity.

Journal of medicinal chemistry·2026
Same journal

Discovery of a Potent NAMPT-Targeting PROTAC for the Suppression of Triple-Negative Breast Cancer via Macrophage Reprogramming.

Journal of medicinal chemistry·2026
See all related articles

This study introduces a new method to extract realistic pair potentials from the Protein Data Bank (PDB) for improved binding affinity prediction. The approach enhances drug design by generating accurate knowledge-based potentials from structural data.

Area of Science:

  • Computational Biophysics
  • Structural Biology
  • Drug Discovery

Background:

  • Knowledge-based potentials are valuable in biophysical studies of macromolecules.
  • Previous self-consistent studies demonstrated extracting pair potentials from model databases.

Purpose of the Study:

  • To extend self-consistent methods for extracting realistic pair potentials from the Protein Data Bank (PDB).
  • To develop an improved method for rapid and accurate binding affinity estimation from structural information.

Main Methods:

  • Utilized a clustering approach to define atom types within the PDB.
  • Developed an optimal effective pairwise potential.
  • Integrated the method into the SMoG drug design package.

Related Experiment Videos

Main Results:

  • Generated simple knowledge-based potentials correlating with experimental binding affinities (R = 0.61) for 118 complexes.
  • Achieved an average unsigned error of 1.5 log Ki units for predictions on 1/3 of the database.
  • Created specialized potentials for protein families, showing strong correlation (R = 0.8-0.9) and low errors (1.1-1.3 log Ki units).

Conclusions:

  • Presented a physically motivated approach for optimizing knowledge-based potentials for binding energy prediction.
  • The method can be integrated into lead discovery protocols.
  • Demonstrated improved accuracy in predicting binding affinities using structural data.