Computer Aided Drug Design Based On Virus Key Protease Of SARS And H5N1

In recent years, more and more dangerous virus breaked out and were pandemic all over the world, such as the HIV, SARS and the current H5N1 avian influenzar virus. The viruses break out all over the word not only causes enormous economic loss and also threaten the healths and life of humankind, even lead the result that some people died of the viruses SARS and H5N1. Especially, the possible epidemic situation of H5N1 had attracted international attention resulting in the research for H5N1 virus and drugs becoming hotspot.The research of bioinformatics shows that previous studies for drug discovery against SARS were mainly targeting on 3C-like proteinase. The recent study shows another important protease, cathepsin-L (CTSL), which has great effect on mediating SARS-CoV entry becoming an alternate drug target against the SARS. A virtual screening of Chinese herb compounds library was performed based on MDL28170's pharmacophore. Eleven Chinese herb compounds are screened and MOL736 has the highest similarity and lowest binding free energy.For H5N1 influenza virus drug-resistance, the structure-activity relationship between NA (neuraminidase) and its three inhibitors (DANA, Zanamivir, and Oseltamivir) was investigated. A homology model of H5N1-NA (BAE46950), the first reported oseltamivir-resistance virus strain, and other 108 homology-modeled 3D structures of chicken influenza H5N1 NAs are the molecular structural basis for the drug-resistance study. The crystal structure of H5N1 avian influenza neuraminidase 2HTY suggests new opportunities for drug design, which reveals the active sites of the group-1 neuraminidases. The 2HTY NA structure contains the N1 subtype, having key difference in the 150-loop cavity adjacent to the conserved active site from those of group-2 neuraminidases. Based on these findings and by modifying oseltamivir, some analog inhibitors were proposed as hopeful candidates for developing inhibitors against H5N1 virus, particularly against the oseltamivir resistant H5N1 virus strain.In cooperation with the fragment-based design, a new drug design method, the so-called"fragment-based quantitative structure-activity relationship"(FB-QSAR), is proposed. The essence of the new method is that the molecular framework in a family of drug candidates is divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard 2D-QSAR is a special case of FB-QSAR when the whole molecule is treated as one fragment. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.