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

Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models.

A Golbraikh1, P Bernard, J R Chrétien

  • 1Laboratory of Chemometrics and Bioinformatics, University of Orléans, BP 6759, 45067, Orléans, France.

European Journal of Medicinal Chemistry
|March 25, 2000
PubMed
Summary

Protein-based alignment (PBA) 3D QSAR models offer superior prediction of acetylcholinesterase (AChE) inhibitor activity compared to structure-based alignment (SBA) models. PBA models maintain high predictability even with increased molecular diversity, unlike SBA models.

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

Rank-based ant system method for non-linear QSPR analysis: QSPR studies of the solubility parameter.

SAR and QSAR in environmental research·2011
Same author

Computer-aided molecular selection and design of natural bioactive molecules.

Current opinion in drug discovery & development·2009
Same author

Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures.

SAR and QSAR in environmental research·2006
Same author

QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors.

SAR and QSAR in environmental research·2005
Same author

Chemometric studies of retention in capillary gas chromatographic separation of hydrocarbons in coupled columns.

Journal of chromatography. A·2005
Same author

[Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds].

Bioorganicheskaia khimiia·2001

Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Pharmacology

Background:

  • Acetylcholinesterase (AChE) inhibitors are crucial for Alzheimer's disease treatment.
  • Comparative Molecular Field Analysis (CoMFA) is a 3D QSAR technique used to predict molecular activity.
  • Protein-based alignment (PBA) and structure-based alignment (SBA) are two common alignment strategies in CoMFA.

Purpose of the Study:

  • To compare the predictive performance of PBA and SBA CoMFA models for AChE inhibitors.
  • To investigate the impact of molecular diversity on the predictability of these models.
  • To identify the optimal alignment strategy for developing reliable 3D QSAR models.

Main Methods:

  • Development of 3D QSAR models for N-benzylpiperidine derivatives targeting AChE.

Related Experiment Videos

  • Application of both PBA and SBA alignment methods for model generation.
  • Utilizing a Kohonen self-organizing map (SOM) to assess molecular diversity.
  • Docking of ligand conformers to the AChE active site for SBA model construction.

    • Main Results:

    • SBA 3D QSAR models showed limited predictive power, restricted to compounds within the training set's molecular diversity.
    • PBA 3D QSAR models demonstrated higher predictability, extending to compounds with greater molecular diversity.
    • The protein's role in automatically selecting the active ligand conformation contributes to PBA's enhanced predictability.

    Conclusions:

    • PBA 3D QSAR models are more robust and predictable for AChE inhibitors than SBA models.
    • The choice of alignment strategy significantly influences the reliability of 3D QSAR predictions.
    • PBA offers a more effective approach for drug discovery targeting AChE, especially when dealing with diverse compound libraries.