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Structural Investigation of Vinca Domain Tubulin Binders by Pharmacophore, Atom based QSAR, Docking and Molecular Dynamics Simulations. | LitMetric

AI Article Synopsis

  • The study focuses on the vinca domain of tubulin proteins as a target for microtubule-targeting drugs (MTD), highlighting the need for a better understanding of its binding mechanisms and structure-activity relationships to aid in drug development.
  • Researchers created a pharmacophore model and conducted various simulations, including QSAR, molecular docking, and molecular dynamics, to identify potential inhibitors that effectively bind to the vinca domain.
  • The developed model showed strong predictive capabilities with high statistical values, and the results indicate that the identified ligands bind stably within the target structure, paving the way for future drug discovery efforts.

Article Abstract

Aim And Objective: Vinca domain of tubulin protein is the potential target for different microtubule targeting drugs (MTD). However, its binding mechanism and structure-activityrelationship (SAR) is not well understood in terms of ligand-receptor interactions and structure functionality requirements. This limits the exploitation of vinca domain for developing novel clinical leads. Herein, as a progressive step towards the exploration of this target, we rendered the in-silico insight through the development of a robust pharmacophore model followed by the QSAR, Molecular Docking and Molecular Dynamics (MD) simulations. Furthermore, the study was undertaken to identify potent inhibitors that can inhibit vinca domain of tubulin.

Materials And Methods: Utilizing the well-defined tubulin polymerization inhibition activities, common pharmacophore hypotheses were constructed and scored for their rankings. The hypotheses were validated by 3D-Atom based QSAR and tested for various statistically relevant metrices. Thereafter, virtual screening was performed with ZINC natural product database and the screened hits were evaluated for structure-based studies via molecular docking and molecular dynamics simulations.

Results: The predictive 3D-QSAR based pharmacophore model consists of two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one hydrophobic (H) group. Significance of the model was reflected from the statistical parameters viz. r2 = 0.98, q2 = 0.72, F = 562.9, RMSE = 0.11 and Pearson-R = 0.87. Further, the docking scores of the retrieved hits deciphered that the ligands were adequately bound in the pocket. Moreover, RMSD fluctuations of protein (1.0 to 1.75A) and ligand (0.3 to 2.3 Å) in molecular dynamics simulations insinuate towards the conformational and interactions stability of the complexes.

Conclusion: The quantitative pharmacophore model was developed from range of natural product scaffolds in order to incorporate all the complimentary features accountable for inhibition. The obtained hits were found to occupy similar binding region and superimpose well over the reference ligand. Therefore, it can be concluded that hierarchical combination of methods exploited in this study can steer the identification of novel scaffolds. Moreover, the rendered hit molecules could serve as potential inhibitory leads for the development of improved inhibitors targeting Vinca domain.

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Source
http://dx.doi.org/10.2174/1386207320666170509151253DOI Listing

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