Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery.In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARSCoV).Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugscontaining the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. Thismeans that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for furtherSARS-CoV main proteinase inhibitor development.

Background: The exact origin of the cause of the Severe Acute Respiratory Syndrome (SARS) isstill an open question. The genomic sequence relationship of SARS-CoV with 30 different singlestrandedRNA (ssRNA) viruses of various families was studied using two non-standard approaches.Both approaches began with the vectorial profiling of the tetra-nucleotide usage pattern V for eachvirus. In approach one, a distance measure of a vector V, based on correlation coefficient wasdevised to construct a relationship tree by the neighbor-joining algorithm. In approach two, amultivariate factor analysis was performed to derive the embedded tetra-nucleotide usage patterns.These patterns were subsequently used to classify the selected viruses.Results: Both approaches yielded relationship outcomes that are consistent with the known virusclassification. They also indicated that the genome of RNA viruses from the same family conformto a specific pattern of word usage. Based on the correlation of the overall tetra-nucleotide usagepatterns, the Transmissible Gastroenteritis Virus (TGV) and the Feline CoronaVirus (FCoV) areclosest to SARS-CoV. Surprisingly also, the RNA viruses that do not go through a DNA stagedisplayed a remarkable discrimination against the CpG and UpA di-nucleotide (z = -77.31, -52.48respectively) and selection for UpG and CpA (z = 65.79,49.99 respectively). Potential factorsinfluencing these biases are discussed.Conclusion: The study of genomic word usage is a powerful method to classify RNA viruses. Thecongruence of the relationship outcomes with the known classification indicates that there existphylogenetic signals in the tetra-nucleotide usage patterns, that is most prominent in the replicaseopen reading frames.

The precise diagnosis of cancer type based on microarray data is of particular importance and is alsoa challenging task. We have devised a novel pattern recognition procedure based on independentcomponent analysis (ICA). Different from the conventional cancer classification methods, which are limitedin their clinical applicability of cancer diagnosis, our method extracts explicitly, by ICA algorithm, a set ofspecific diagnostic patterns of normal and tumor tissues corresponding to a set of biomarkers for clinicaluse. We validated our procedure with the colon and prostate cancer data sets and achieved good diagnosis(490%) on the data sets studied here. This technique is also suitable for the identification of diagnosticexpression patterns for other human cancers and demonstrates the feasibility of simple and accuratemolecular cancer diagnostics for clinical implementation.

Based on molecular modeling techniques we constructed a rational 3D model of ORF3 in SARS coronavirus (SARS-CoV). Our studies suggest that the function of ORF3 could be involved in FAD/NAD binding according to its predicted structure and comparison with other structure neighbors. Furthermore, we identified three pairs of non-canonical N–H/p interactions in the structure of ORF3, which can make contributions to the stability of protein structure. These results provide important clues for better understanding of SARS-CoV ORF3 and trying new therapeutic strategies.