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

MOPED: method for optimizing physical energy parameters using decoys.

Chaok Seok1, J B Rosen, John D Chodera

  • 1Department of Pharmaceutical Chemistry, University of California in San Francisco, San Francisco, California 94118, USA.

Journal of Computational Chemistry
|December 17, 2002
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

Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.

Nature communications·2026
Same author

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.

Journal of chemical information and modeling·2026
Same author

MotifLeadDB: A Hierarchical Structural Data Set for Congeneric Ligand Binding Activity Change.

Journal of chemical information and modeling·2026
Same author

Structural basis of the sweat odorant HMHA recognition by the human odorant receptor OR52E8.

Communications biology·2026
Same author

Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.

Journal of chemical theory and computation·2026
Same author

GalaxyCDock: Webserver for Covalent Protein-Ligand Binding Mode Prediction.

Journal of molecular biology·2026
Same journal

OpenCafeMol With 3SPN.2 DNA Model: GPU Acceleration for Long-Time Coarse-Grained Chromatin Simulations.

Journal of computational chemistry·2026
Same journal

Nuclear Quantum Effects on the Organic Bifurcation Reaction in Microsolvated Water Clusters: Ring-Polymer Molecular Dynamics Calculations Using an Explicit Solvation Model.

Journal of computational chemistry·2026
Same journal

Computational Analysis of the (4+3) Cycloaddition Reaction of a Sulfoximine-Stabilized Oxyallylic Cation With Furan.

Journal of computational chemistry·2026
Same journal

Reaction Enumeration Based on NBO-Informed Molecular Graphs.

Journal of computational chemistry·2026
Same journal

How Do DICER1 Syndrome Mutations Disrupt Catalysis? Unveiling Dicer Metal Binding Architecture and Mechanism of Action Using MD Simulations and QM/MM Calculations.

Journal of computational chemistry·2026
Same journal

Quadruple Bonding of Alkaline Earth Atoms in AeCLi<sub>4</sub> (Ae = Be - Ba) Complexes.

Journal of computational chemistry·2026
See all related articles

We developed MOPED, a new method for optimizing computational models. MOPED improves energy functions, enhancing the accuracy of protein structure prediction by refining energetic and structural parameters.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Biophysics

Background:

  • Accurate protein structure prediction is crucial for understanding biological function.
  • Existing computational models often struggle with parameter optimization, particularly for complex energy functions.
  • Distinguishing native protein structures from decoys remains a significant challenge.

Purpose of the Study:

  • To introduce MOPED (Model Optimization of Parameters for Energy Discrimination), a novel method for optimizing parameters in computational models.
  • To address limitations in current methods by handling nonlinear energy functions and continuous degrees of freedom.
  • To improve the accuracy of protein structure discrimination using enhanced energy functions.

Main Methods:

  • MOPED optimizes energetic and structural parameters using native structures and decoys.

Related Experiment Videos

  • The method is applied to the EEF1 energy function, which combines the CHARMM19 force field with a Gaussian solvation term.
  • MOPED handles energy functions nonlinear in parameters and continuous in degrees of freedom.

    • Main Results:

    • MOPED successfully improves solvation parameters for the EEF1 energy function.
    • The enhanced EEF1 parameters demonstrate improved discrimination between native and decoy protein structures.
    • The method shows effectiveness across various decoy sets, including challenging ones from Baker et al.

    Conclusions:

    • MOPED provides a robust approach for optimizing computational models in structural biology.
    • Improved energy function parameters enhance the reliability of protein structure prediction.
    • The method is applicable to a wide range of protein structures, excluding those with metals or prosthetic groups.