Biomedical subjects
Publications and source records attributed to Stefan Balaz.
MMP inhibitors with novel oxazoline zinc binding groups have been synthesized and evaluated. Selectivity for the inhibition of MMP-9 over MMP-1, MMP-2, and MMP-12 has been achieved in several cases.
Tissue components hydrolyzing matrix metalloproteinases (MMPs) exhibit a high sequence similarity (56-64% in catalytic domains) and yet a significant degree of functional specificity. The hexapeptide-binding sites of 24 known human MMPs were compared in terms of their force field interaction energies with five probes that are most frequently encountered in substrates and inhibitors. The probes moved along a grid enclosing partially flexible binding sites in rigid catalytic domains that were represented by published experimental structures and comparative models and new comparative models for nine most recently characterized MMPs. For individual MMPs, representative interaction energies were obtained as averages for all suitable experimental structures. Correlations of the representative energies for all MMP pairs were succinctly catalogued for individual probes, subsites, and correlation levels. Among the probes (neutral sp(3) carbon and sp(3) oxygen, positive sp(3) nitrogen and hydrogen, and negative carbonyl oxygen), the last probe is least distinctive. Similarities of subsites are decreasing as S1 ' > S2 > S3 ' > S1 approximately S3 > S2 '. Most interesting, occupancies of subsites in published structures of MMP-inhibitor complexes follow an almost parallel trend, alluding to overall low selectivity of known MMP inhibitors. Flexible subsite S1 ' that appears as the specificity pocket in rigid x-ray structures is actually very similar among individual MMPs. Several correlations indicated that MMPs 3, 8, and 12 have similar binding sites. Modeling results are corroborated with published experimental data on MMP inhibition and substrate specificities. The results provide numerous clues for development of specific inhibitors and substrates, as well as for selection of MMPs for testing that provides maximum information without redundant experiments.
Forty zinc-dependent metalloproteinase/ligand complexes with known crystal structures were re-docked using five docking/scoring approaches (DOCK, FlexX, DrugScore, GOLD, and AutoDock). Correct geometry of the coordination bonds between the ligand's zinc binding group (ZBG) and the catalytic zinc is important for docking accuracy and scoring reliability. More than 75% of docked poses with RMSD less than 2A were found to have appropriate ZBG binding, but for poor ZBG binding, about 95% of poses failed to dock correctly. Elimination of poses with inappropriate zinc binding resulted in better binding energy predictions that were further improved by dividing the ligands into subsets according to the ZBG (carboxylates, hydroxamates, and phosphorus containing groups). After a subset re-scoring using the regression functions obtained for individual subsets, DrugScore was able to explain 77% and the consensus scoring scheme X-CSCORE even 88% of variance in binding energies. The approach combining ZBG-based pose selection and subset re-scoring improved the hit rate in virtual screening for metalloproteinase inhibitors for all tested methods by 4-16%.
Subcellular pharmacokinetics (SP) optimizes biology-related factors in the design of libraries for high throughput screening by defining comparatively narrow ranges of properties (lipophilicity, amphiphilicity, acidity, reactivity, 3D-structural features) of the included compounds. The focusing ensures appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) in those test biosystems, which are more complex than isolated receptors, and in humans. The SP deploys conceptual models that include transport and accumulation in a series of membranes, protein binding, hydrolysis, and other reactions with cell constituents. The kinetics of drug disposition is described as a non-linear disposition function of drug structure and properties. The SP capabilities are illustrated here using a model-based quantitative structure-activity relationship of toxicity of phenolic compounds against Tetrahymena pyriformis as dependent on lipophilicity and acidity. The resulting SP models clearly outperform empirical models in predictive ability outside the parameter space, as revealed by the leave-extremes-out cross-validation technique with omission of compounds beyond pre-defined lipophilicity and acidity ranges. The SP models do not change substantially if the parameters space is shrunk within some limits. In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization. Once calibrated for a given biosystem, the SP models provide a detailed recipe for tailoring the drug properties to ensure optimum ADMET. The focusing is more accurate than with traditional empirical QSAR studies, assessment of drug-likeness, or the rules for identification of compounds with permeability problems.
Receptor site modeling methods usually use one binding mode (conformation and/or orientation) for each ligand in a 1:1 complex with receptor. Multiple modes should be considered instead because (1). they have frequently been observed experimentally; (2). in a series, ligands can bind in single yet different modes; and (3). a series may only exhibit one but unknown mode and a few plausible modes must be examined. For multimode binding, the observed ligand/receptor association constant is the sum of the association constants that characterize individual binding modes. This relation, when applied to Comparative Molecular Field Analysis (CoMFA), results in a dependence of the observed binding energy on the probe energies that is nonlinear in optimized parameters. The dependence was linearized to allow parameter optimization by the partial least-squares method that was used iteratively until self-consistency. In addition to the standard CoMFA output, the procedure objectively selects one or a few optimal binding modes out of a dozen or more modes that are considered for each ligand. The approach was applied to published data for binding of 34 polychlorinated dibenzofurans to the aryl hydrocarbon receptor. Descriptive and predictive abilities of the 16-mode model were significantly better than for the one-, two-, and four-mode models. Predominantly, edge-aligned modes were selected that are seldom used in CoMFA. Since inclusion of multimode binding only changes the form of the correlation equation and does not affect the number of optimized parameters, the improvement is believed to be due to a more realistic description.