No abstract

We investigate whether a molecularly imprinted polymer (MIP) of influenza A H5N1 could be used to help identify molecules capable of binding to, and inhibiting the function of the virus, via either competitive or allosteric mechanisms.

The virtual screening approach for docking small molecules into a known protein structure is a powerful tool for drug design. In this work, a combined docking and neural network approach, using a self-organizing map, has been developed and applied to screen anti-HIV-1 inhibitors for two targets, HIV-1 RT and HIV-1 PR, from active compounds available in the Thai Medicinal Plants Database. Based on nevirapine and calanolide A as reference structures in the HIV-1 RT binding site and XK-263 in the HIV-1 PR binding site, 2,684 compounds in the database were docked into the target enzymes. Self-organizing maps were then generated with respect to three types of pharmacophoric groups. The map of the reference structures were then superimposed on the feature maps of all screened compounds. Only the structures having similar features to the reference compounds were accepted. By using the SOMs, the number of candidates for HIV-1 RT was reduced to six and nine compounds consistent with nevirapine and calanolide A, respectively, as references. For the HIV-1 PR target, there are 135 screened compounds showed good agreement with the XK-263 feature map. These screened compounds will be further tested for their HIV-1 inhibitory affinities. The obtained results indicate that this combined method is clearly helpful to perform the successive screening and to reduce the analyzing step from AutoDock and scoring procedure.

BackgroundIt is known that the highly pathogenic avian influenza A virus H5N1 binds strongly and with high specificity to the avian-type receptor by its hemagglutinin surface protein. This specificity is normally a barrier to viral transmission from birds to humans. However, strains may emerge with mutated hemagglutinin, potentially changing the receptor binding preference from avian to human-type. This hypothesis has been proven correct, since viral isolates from Vietnam and Thailand have been found which have increased selectivity toward the human cell receptor. The change in binding preference is due to mutation, which can be computationally modelled. The aim of this study is to further explore whether computational simulation could be used as a prediction tool for host type selectivity in emerging variants.ResultsMolecular dynamics simulation was employed to study the interactions between receptor models and hemagglutinin proteins from H5N1 strains A/Duck/Singapore/3/97, mutated A/Duck/Singapore/3/97 (Q222L, G224S, Q222L/G224S), A/Thailand/1(KAN-1)/2004, and mutated A/Thailand/1(KAN-1)/2004 (L129V/A134V). The avian receptor was represented by Siaα(2,3)Gal substructure and human receptor by Siaα(2,6)Gal. The glycoside binding conformation was monitored throughout the simulations since high selectivity toward a particular host occurs when the sialoside bound with the near-optimized conformation.ConclusionThe simulation results showed all hemagglutinin proteins used the same set of amino acid residues to bind with the glycoside; however, some mutations alter linkage preferences. Preference toward human-type receptors is associated with a positive torsion angle, while avian-type receptor preference is associated with a negative torsion angle. According to the conformation analysis of the bound receptors, we could predict the relative selectivity in accordance with in vitro experimental data when disaccharides receptor analogs were used.

Thailand has a vast number of plant species. Up to 3000 of them are believed by traditional Thai medicine to possess some biological activity with which researchers have attempted for many years to identify and formulate new drugs. Many chemical compounds from Thai plant species are identified and tested for biological activity that may enable them to be declared lead compounds in drug discovery. Modern methods of drug discovery are rarely used to rationalize and speed-up the process. Within this decade, the first structural database of Thai medicinal plants, Chemiebase, has been built as a platform for virtual screening, using knowledge from Thai traditional medicine. Although this effort is a promising protocol which can be used to validate Thai traditional medicine, there exists another problem that should be resolved before proceeding: It is almost impossible to trace the knowledge to its primary source. Thai traditional knowledge has been passed on orally or - less frequently - in ancient texts. We have built another database, the Thai Herbal Repository Access Initiative (THRAI) database, in order to compile the traditional knowledge into electronic format suitable for the drug design process. Three examples using data from these databases and other computer-aided drug discovery methods to rationalize Thai traditional medicine are presented here, starting with virtual screening exercised on anti-HIV-1 reverse transcriptase, anti-HIV-1 protease, anti-influenza A neuraminidase, and anti-cyclooxygenase (COX), candidates. The second example consists of the use of molecular modeling to propose drug mechanism for anti-tumor compounds. The last one is the study on toxicity assessment of some compounds from Thai medicinal plants.