Computer-Aided Drug Design Based On Novel Antifungal Drug Target

During the past two decades, the life threatening infections caused by pathogenic fungi are becoming increasingly common, especially in those individuals with immunocompromised hosts, such as patients undergoing anticancer chemotherapy or organ transplants and patients with AIDS. However, the current antifungal therapy can suffer from drug related toxicity, severer drug resistance, non-optimal pharmacokinetics and serious drug-drug interactions. Therefore, there is an emergent need to develop novel antifungal drugs with higher efficiency, broader-spectrum and lower toxicity. N-myristoyltransferase (NMT) is a novel antifungal drug target for the development of antifungal agents with new mode of action. 3D-QSAR and molecular docking studies were performed on published NMT inhibitors, and important regions and residues in the active site were identified by MCSS and ET analysis. On the basis of the results from molecular modeling, virtual high-throughput screening were performed and several potential novel NMT inhibitors were discovered.1. 3D-QSAR Study of a Series of Novel benzofuran NMT InhibitorsComparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis(CoMSIA) three dimensional structure-activity relationship (3D-QSAR) studies were conducted on a series of benzofuran NMT inhibitors. Variation of grid spacing was used during the optimization of the CoMFA model. For the CoMSIA study, the influence of the combination of different field types was evaluated and the best combination was considered to be steric, electrostatic, hydrophobic and H-bonding acceptor fields. Variation of grid spacing and attenuation factor was used to get the best CoMSIA model. The resulting CoMFA and CoMSIA models had a cross validated coeffiecient(q~2) of 0.759 and 0.730 respectively, which showed strong predictive ability on both test-set and training-set. The 3D contour maps of CoMFA and CoMSIA provided smooth and interpretable explanation of the structure- activity relationship of the compounds. The analysis of the 3D contour maps permitted interesting conclusions about the effects of different substituent groups of different position of the benzofuran group on the antifungal activity, whichwill guide the design of novel NMT inhibitors with higher activity.2. 3D-QSAR and molecular docking studies on benzothiazole derivatives as Candida albicans N-myristoyltransferase inhibitorsMolecular docking and 3D-QSAR methods, CoMFA and CoMSIA, were applied to a set of novel benzothiazole Candida albicans N-myristoyltransferase (CaNmt) inhibitors. The binding mode of the compounds at the active site of CaNmt was explored using flexible docking method and various hydrophobic and hydrogen-bonding interactions were observed between the benzothiazole inhibitors and the target enzyme. Variations of parameters were considered during the optimization of 3D-QSAR models. The best CoMFA and CoMSIA models had a cross-validated coefficient q2 of 0.670 and 0.732 respectively, which showed high correlative and predictive abilities on both test set and training set. The 3D contour maps of CoMFA and CoMSIA provided smooth and interpretable explanation of the structure-activity relationship for the compounds. The analysis of the 3D contour plots permitted interesting conclusions about the effects of different substituent groups at different position of the benzothiazole ring, which will guide the design of novel CaNmt inhibitors with higher activity.3. MCSS Functionality Maps of Drug Binding Sites for Candida albicans N-MyristoyltransferaseIn order to know the important functional residues and regions in the active site of Candida albicans N-myristoyltransferase (CaNMT) in detail, multiple copy simultaneous search (MCSS) was used to identify the hydrophobic pockets, hydrogen-bonding sites and electrostatic negative sites. The results from MCSS calculation revealed that there were two hydrophobic pockets. One pocket was lined with TyrlO7, Tyrll9, Vail08, Phell7, Phel23, Alal27, Phel76 and Leu337 and the other pocket was lined with Phell5, Phe240 and Phe339. Moreover, two hydrogen-bonding sites were identified by MCSS calculations. Among those hydrogen-bonding residues, Tyrll9, His227, Asn392 and Leu451 could form hydrogen bond with the benzofuran inhibitors and TyrlO7, Asnl75, Thr211 andAsp412 were newly discovered hydrogen-bonding residues, which were high conserved residues across the NMT family and would play an important role to design NMT inhibitors with novel chemical scaffold. Important functional residue Leu451 could serve as both hydrogen-bonding site and electrostatic negative site, which was indispensable in inhibitor design. The above results could provided important clues for the de novo design and virtual high-throughput screening of novel NMT inhibitors.4. Evolutionary trace analysis of NMT familyMultiple sequence alignments were performed on the NMT family and thus evolutionary trace was constructed. The important functional residues of NMT family were identified by the ET analysis. From the ET results, the important residues confirmed by biological experiments in myristoyl CoA binding site, catalytic center and inhibitor binding site were identified successfully. The trace residues were mapped onto the active site of CaNMT. Tip 126, Asnl75 and Thr211 are highly conserved trace residues and do not interact with current NMT inhibitors, which are potential novel drug binding sites for the novel inhibitor design. Pro338, Leu350, Ile352 and Ala353 are class-specific trace residues, which are important for the optimization of current NMT inhibitors. The trace residues identified by ET analysis are of great importance to study the structure-function relationship and also design specific inhibitors.5. Virtual high-throughput screening for the discovery of novel NMT inhibitorsOn the basis of the identified important drug binding sites by MCSS and ET analysis, virtual high-throughput screening of SPECS database was performed in order to find novel NMT inhibitors. The benzuofuran inhibitor was identified successfully during the virtual screening, which confirmed our screening strategy. Twenty-five compounds were selected by two docking methods in combination of consensus scoring and drug-likeness evaluation, which would be potential NMT inhibitors with novel chemical scaffolds.