Abstract ▼ AI-Helper

Peptide deformylase (PDF) is a metalloenzyme, which catalyzes the removal of a formyl group from the N-formylmethionine polypeptide. The essentiality of this enzyme in bacterial cells but not in mammalian cells has made it an attractive and new target for the development of antibacterial agents. Qua...

Peptide deformylase (PDF) is a metalloenzyme, which catalyzes the removal of a formyl group from the N-formylmethionine polypeptide. The essentiality of this enzyme in bacterial cells but not in mammalian cells has made it an attractive and new target for the development of antibacterial agents. Quantitative Structure-Activity Relationships (QSAR) analyses of a series of reverse hydroxamate derivatives with antibacterial activities against Escherichia coli PDF published by British Biotech has been carried out by three methods, Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and Hologram QSAR (HQSAR). Statistically reliable models were generated from all the three methods with good predictive power (CoMFA r2 = 0.957, q2 = 0.570; CoMSIA r2 = 0.952, q2 = 0.503; HQSAR r2 = 0.939, q2 = 0.559). The predictability of these models were validated by using a set of compounds that were not included in the training set giving satisfactory predictive r2 values of 0.681, 0.673, and 0.424, respectively. In this case, 3D prediction models showed better predictive power than 2D model for test set. It might be due to that 3D information is important because four series of compounds with similar backbones are used as dataset. Superposition of the CoMFA contour maps with the actinonin bound crystal structure in active site of PDF showed meaningful correlation between receptor-ligand binding and their biological activities. The final QSAR models along with the information gathered from 3D contour and 2D contribution map could be useful for the design of novel active inhibitors of PDF having improved potency.

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