We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% assessed by a rigorous full Jack-knife procedure! on a new nonredundant dataset of 496 nonhomologous sequences obtained from G.J. Barton and J.A. Cuff !. This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes~H, E, C! with an accuracy of up to 78% for b-strands, using only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of b-strands has been probably previously overestimated.
Styrylquinoline derivatives, known to be potent inhibitors of HIV-1 integrase, have been experimentally tested for their inhibitory effect on the disintegration reaction catalyzed by catalytic cores of HIV-1 and Rous sarcoma virus (RSV) integrases. A modified docking protocol, consisting of coupling a grid search method with full energy minimization, has been specially designed to study the interaction between the inhibitors and the integrases. The inhibitors consist of two moieties that have hydroxyl and/or carboxyl substituents: the first moiety is either benzene, phenol, catechol, resorcinol, or salicycilic acid; the hydroxyl substituents on the second (quinoline) moiety may be in the keto or in the enol forms. Several tautomeric forms of the drugs have been docked to the crystallographic structure of the RSV catalytic core. The computed binding energy of the keto forms correlates best with the measured inhibitory effect. The docking procedure shows that the inhibitors bind closely to the crystallographic catalytic Mg(2+) dication. Additional quantum chemistry computations show that there is no direct correlation between the binding energy of the drugs with the Mg(2+) dication and their in vitro inhibitory effect. The designed method is a leading way for identification of potent integrase inhibitors using in silico experiments.
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