No abstract

Valproic acid (VPA) is a short, branched fatty acid with broad-spectrum anticonvulsant activity. It has been suggested that VPA acts directly on the plasma membrane. We calculated the free energy of interaction of VPA with a model lipid bilayer using simulated annealing and the continuum solvent model. Our calculations indicate that VPA is likely to partition into the bilayer both in its neutral and charged forms, as expected from such an amphipathic molecule. The calculations also show that VPA may migrate (flip-flop) across the membrane; according to our (theoretical) study, the most likely flip-flop path at neutral pH involves protonation of VPA pending its insertion into the lipid bilayer and deprotonation upon departure from the other side of the bilayer. Recently, the flip-flop of long fatty acids across lipid bilayers was studied using fluorescence and NMR spectroscopies. However, the measured value of the flip-flop rate appears to depend on the method used in these studies. Our calculated value of the flip-flop rate constant, 20/s, agrees with some of these studies. The limitations of the model and the implications of the study for VPA and other fatty acids are discussed.

This study describes the development of aptamers as a therapy against influenza virus infection. Aptamers are oligonucleotides (like ssDNA or RNA) that are capable of binding to a variety of molecular targets with high affinity and specificity. We have studied the ssDNA aptamer BV02, which was designed to inhibit influenza infection by targeting the hemagglutinin viral protein, a protein that facilitates the first stage of the virus’ infection. While testing other aptamers and during lead optimization, we realized that the dominant characteristics that determine the aptamer’s binding to the influenza virus may not necessarily be sequence-specific, as with other known aptamers, but rather depend on general 2D structural motifs. We adopted QSAR (quantitative structure activity relationship) tool and developed computational algorithm that correlate six calculated structural and physicochemical properties to the aptamers’ binding affinity to the virus. The QSAR study provided us with a predictive tool of the binding potential of an aptamer to the influenza virus. The correlation between the calculated and actual binding was R2 = 0.702 for the training set, and R2 = 0.66 for the independent test set. Moreover, in the test set the model’s sensitivity was 89%, and the specificity was 87%, in selecting aptamers with enhanced viral binding. The most important properties that positively correlated with the aptamer’s binding were the aptamer length, 2D-loops and repeating sequences of C nucleotides. Based on the structure-activity study, we have managed to produce aptamers having viral affinity that was more than 20 times higher than that of the original BV02 aptamer. Further testing of influenza infection in cell culture and animal models yielded aptamers with 10 to 15 times greater anti-viral activity than the BV02 aptamer. Our insights concerning the mechanism of action and the structural and physicochemical properties that govern the interaction with the influenza virus are discussed.

The identification of ensembles of bioactive conformers of drug-like compounds is far from being a solved problem. Recent research has advanced the field to the point where bioactive conformers could be readily identified from within conformational ensembles generated by contemporary computational tools. However, as such conformers are inevitably accompanied by many other non-relevant conformations, a focusing mechanism is required. New methods in this field are showing promise but more work is clearly needed. New research lines are proposed which are believed to enhance the performances and with it the usefulness of 3D ligand-based methods in drug discovery and development.

Computational approaches that rely on ligand-based information for lead discovery and optimization are often required to spend considerable resources analyzing compounds with large conformational ensembles. In order to reduce such efforts, we have developed a new filtration tool which reduces the total number of ligand conformations while retaining in the final set a reasonable number of conformations that are similar (rmsd < or = 1 A) to those observed in ligand-protein cocrystals (bioactive-like conformations). Our tool consists of the following steps: (1) Prefiltration aimed at removing ligands for which the probability of finding bioactive-like conformations is low. (2) Filtration based on a unique combination of two-/three-dimensional ligand properties. Within this paradigm, a filtration model is defined by its workflow and by the identity of the specific descriptors used for filtration. Thus, we developed multiple models based on a training set of 47 drug compounds and tested their performance on an independent test set of 24 drug compounds. For test set compounds after prefiltration, our best models have a success rate of approximately 80% and were able to reduce the total number of conformations by 36% while maintaining a sufficiently large number of bioactive-like conformations and slightly increasing their proportion in the filtered ensemble. We were also able to reduce by 39% the number of conformations that are remote (rmsd > 2.5 A) from the bioactive conformer (nonbioactive conformations). In accord with previous reports, prefiltration is shown to have a major effect on model performance. The role and performance of specific descriptors as filters is discussed in some detail, and future directions are proposed.