Designing And Screening Of Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitors

AIDS has become one of the most serious epidemic diseases that threat to human health and life. Non-nucleoside reverse transcriptase inhibitors (NNRTIs) with structural diversity have, in addition to other kinds of drugs, gained a significant place in the treatment of HIV-1 infections because of their low toxicity, high potency,and statistical synergy with other anti-HIV drugs. However, an obvious disadvantage of NNRTIs is rapid emergence of virus-drug resistance due to mutations of the amino acids surrounding the NNRTI-binding site. Therefore, it is critical to explore novel types of NNRTIs effective against drug-resistance.Computer-aided drug design (CADD) is a drug design method based on priciples of computational chemistry which rationally design new type of structural lead compound through analyzing relationship between structure and activity of available drugs or modeling interaction between drug and bio-molecule receptor.Objective: This research was focused on seeking a novel class of potent NNRTIs by virtual screening from the traditional Chinese Chemistry Medicines database (TCMD) based on the structure of target binding site and the pharmacophore constructed by the second generation of NNRTIs.Method: Computer aided approaches have been proved to be effective in making qualitative predictions that discriminated active from inactive compounds. In this research, firstly, pharmacophore models were characterized using a training set of the second generation of NNRTIs. The best model was used as database search queries to identify active compounds for HIV-1 RT from the Traditional Chinese Medicine Database (TCMD). The search was carried out in sybyl which the donor site of hydrogen bond in protein was also defined. Secondly, molecule dock are being used with Molegro Virtual Docker (MVD) for understanding of structural and energetic properties of the compounds, in particular, hydrogen bonding, polarization effects and even realistic models of drug–enzyme interaction. The results were reranked by the scoring function of the docking tool. Finally, molecular simulations are used for predicting the dynamics properties of the enzyme and inhibitors based on the docking results.Result:(1) Pharmacophore perception and database screening Three point pharmacophore model was characterized based on the three molecular, which include two hydrophobic centers and a acceptor atom of hydrogen bond. The distance between them were 4.81nm, 6.63nm and 4.86nm. The tolerance of each distances were defined as 1nm in case of the flexible of NNIBP. Finally 476 compounds were found from TCMD by the three point pharmacophore model. (2) Molecular dockingWe docked them to the wild (2ZD1) (PDB code http://www.pdb.org/) and mutational (3BGR) HIV-1 RT. The results were ranked by scoring function. Kurarinol was identified because of it ranked first and second in the docking results of 2ZD1 and 3BGR. Additional hydrogen bond and the flips of hydrocarbon chain were found between the hydroxy groups in M4753 in 3BGR docking model. The hydrogen bond and the flip of hydrocarbon chain were important for anti-resistant mutation. Molecular dynamic simulation results showed that average atom movement were small, fast convergence of energy were observed. Several stable hydrogen bonds were generated between the binding.Conclusion: This highlights the stable binding of Kurarinol with NNIBP, and might provide useful clues for designing of powerful durgs against HIV-1 RT.