Title:Molecular Docking and QSAR Studies of Coumarin Derivatives as NMT Inhibitors: Simple Structural Features as Potential Modulators of Antifungal Activity
VOLUME: 17 ISSUE: 10
Author(s):Sapna Jain Dabade, Dheeraj Mandloi* and Amritlal Bajaj
Affiliation:Department of Applied Science, SAGE University, Indore & Research Scholar at School of Chemical Sciences, Devi Ahilya Vishwavidyalaya, Indore, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore, School of Chemical Sciences, Devi Ahilya Vishwavidyalaya, Indore
Keywords:QSAR, coumarin analogue, GA-MLR, molecular docking, Candida albicans, NMT inhibitors.
Abstract:
Background: Treatments of fungal diseases, including Candidiasis, remain not up to
scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the
impetus for investigating new antifungal agents based on natural products. Consequently, genetic
algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship)
studies of coumarin analogues along with molecular docking were carried out.
Methods: Coumarin analogues with their MIC values were used to generate the training and test
sets of compounds for QSAR models development; the analogues were also docked into the binding
pocket of NMT (MyristoylCoA: protein N-myristoyltransferase).
Results and Discussion: The statistical parameters for internal and external validation of QSAR
analysis (R2 = 0.830, Q2 = 0.758, R2Pred = 0.610 and R2m overall = 0.683 ), Y Randomization, Ridge
trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of
the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic
clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most
active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding
pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226,
Cys393 and Leu394 amino acid residues.
Conclusion: The combinations of descriptors from various descriptor subsets in QSAR analysis
have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume
to explain the inhibitory activity. The models and related information may pave the way for
important insight into the designing of putative NMT inhibitors for Candida albicans.