A novel coronavirus has been identified as an etiological agent of severe acute respiratory syndrome (SARS). To rapidly identify anti-SARS drugs available for clinical use, we screened a set of compounds that included antiviral drugs already in wide use. Here we report that the HIV-1 protease inhibitor, nelfinavir, strongly inhibited replication of the SARS coronavirus (SARS-CoV). Nelfinavir inhibited the cytopathic effect induced by SARS-CoV infection. Expression of viral antigens was much lower in infected cells treated with nelfinavir than in untreated infected cells. Quantitative RT-PCR analysis showed that nelfinavir could decrease the production of virions from Vero cells. Experiments with various timings of drug addition revealed that nelfinavir exerted its effect not at the entry step, but at the post-entry step of SARS-CoV infection. Our results suggest that nelfinavir should be examined clinically for the treatment of SARS and has potential as a good lead compound for designing anti-SARS drugs.
We have developed a visualized cluster analysis of protein-ligand interaction (VISCANA) that analyzes the pattern of the interaction of the receptor and ligand on the basis of quantum theory for virtual ligand screening. Kitaura et al. (Chem. Phys. Lett. 1999, 312, 319-324.) have proposed an ab initio fragment molecular orbital (FMO) method by which large molecules such as proteins can be easily treated with chemical accuracy. In the FMO method, a total energy of the molecule is evaluated by summation of fragment energies and interfragment interaction energies (IFIEs). In this paper, we have proposed a cluster analysis using the dissimilarity that is defined as the squared Euclidean distance between IFIEs of two ligands. Although the result of an ordered table by clustering is still a massive collection of numbers, we combine a clustering method with a graphical representation of the IFIEs by representing each data point with colors that quantitatively and qualitatively reflect the IFIEs. We applied VISCANA to a docking study of pharmacophores of the human estrogen receptor alpha ligand-binding domain (57 amino acid residues). By using VISCANA, we could classify even structurally different ligands into functionally similar clusters according to the interaction pattern of a ligand and amino acid residues of the receptor protein. In addition, VISCANA could estimate the correct docking conformation by analyzing patterns of the receptor-ligand interactions of some conformations through the docking calculation.
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