A Combined QSAR and Molecular Docking Approach for Identifying Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors

Background: Antimicrobial resistance has been rising continuously in the past few years due to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due to their widespread biological activities, such as anti-bacterial and antifungal activities. Objective: The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D quantitative structure-activity relationship analysis (2D QSAR) and docking analysis. Methods: The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis. Results: The best QSAR model displays the r(t)(2) value of 0.594, Q(LOO)(2) value of 0.779, Q(L5O)(2) value of 0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein (PBP2a). Conclusion: From the results, Compounds 4, 11and 24 were found to possess a good binding affinity towards PBP2a.

Automated summary

This study investigates the use of pyrimidine-based antimicrobial agents for their rapid antimicrobial action. The 2D quantitative structure-activity relationship analysis and docking analysis were performed to identify the compounds with good binding affinity towards Penicillin-binding protein (PBP2a). The results show that compounds 4, 11, and 24 exhibit strong binding affinity towards PBP2a.