Virtual affinity fingerprints for target fishing: a new application of Drug Profile Matching.

We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature.

Temperature dependence of internal friction in enzyme reactions.

Our aim was to elucidate the physical background of internal friction of enzyme reactions by investigating the temperature dependence of internal viscosity. By rapid transient kinetic methods, we directly measured the rate constant of trypsin 4 activation, which is an interdomain conformational rearrangement, as a function of temperature and solvent viscosity. We found that the apparent internal viscosity shows an Arrhenius-like temperature dependence, which can be characterized by the activation energy of internal friction. Glycine and alanine mutations were introduced at a single position of the hinge of the interdomain region to evaluate how the flexibility of the hinge affects internal friction. We found that the apparent activation energies of the conformational change and the internal friction are interconvertible parameters depending on the protein flexibility. The more flexible a protein was, the greater proportion of the total activation energy of the reaction was observed as the apparent activation energy of internal friction. Based on the coupling of the internal and external movements of the protein during its conformational change, we constructed a model that quantitatively relates activation energy, internal friction, and protein flexibility.

Contribution of 2D and 3D structural features of drug molecules in the prediction of Drug Profile Matching.

Drug Profile Matching (DPM), a novel virtual affinity fingerprinting method capable of predicting the medical effect profiles of small molecules, was introduced by our group recently. The method exploits the information content of interaction patterns generated by flexible docking to a series of rigidly kept nontarget protein active sites. We presented the ability of DPM to classify molecules excellently, and the question arose, what the contribution of 2D and 3D structural features of the small molecules is to the intriguingly high prediction power of DPM. The present study compared the prediction powers for effect profiles of 1163 FDA-approved drug compounds determined by DPM and ChemAxon 2D and 3D similarity fingerprinting approaches. We found that DPM outperformed the 2D and 3D approaches in almost all therapeutic categories for drug classification except for mechanically rigid structural categories where high accuracy was obtained by all three methods. Moreover, we also tested the predictive power of DPM on external data by reducing the parent data set and demonstrated that DPM can overcome the common screening problems of 2D and 3D similarity methods arising from the presence of structurally diverse molecules in certain effect categories.

Drug effect prediction by polypharmacology-based interaction profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.

Relating the shape of protein binding sites to binding affinity profiles: is there an association?

BACKGROUND: Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses. RESULTS: PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors. CONCLUSIONS: This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.

Site directed mutagenesis at position 193 of human trypsin 4 alters the rate of conformational change during activation: role of local internal viscosity in protein dynamics.

Upon activation of trypsinogen four peptide segments flanked by hinge glycine residues undergo conformational changes. To test whether the degree of conformational freedom of hinge regions affects the rate of activation, we introduced amino acid side chains of different characters at one of the hinges (position 193) and studied their effects on the rate constant of the conformational change. This structural rearrangement leading to activation was triggered by a pH-jump and monitored by intrinsic fluorescence change in the stopped-flow apparatus. We found that an increase in the size of the side chain at position 193 is associated with the decrease of the reaction rate constant. To analyze the thermodynamics of the reaction, temperature dependence of the reaction rate constants was examined in a wide temperature range (5-60 degrees C) using a novel temperature-jump/stopped-flow apparatus developed in our laboratory. Our data show that the mutations do not affect the activation energy (the exponential term) of the reaction, but they significantly alter the preexponential term of the Arrhenius equation. The effect of solvent viscosity on the rate constants of the conformational change during activation of the wild type enzyme and its R193G and R193A mutants was determined and evaluated on the basis of Kramers' theory. Based on this we propose that the reaction rate of this conformational transition is regulated by the internal molecular friction, which can be specifically modulated by mutagenesis in the hinge region.

Modeling Polypharmacological Profiles by Affinity Fingerprinting.

Single target based approaches often proved to be unsuccessful in complex multigenic diseases such as cancer or schizophrenia. Multi-target drugs can be more efficacious in this regard by modulating multiple processes in the organism. According to the theory of polypharmacology, bioactive molecules possess characteristic interaction patterns that are responsible for their effects and side-effects and getting acquainted with this typical profile is increasingly desired to promote pharmaceutical research and development. There is a novel way of approaching polypharmacology that takes into account the interaction of molecules to a set of proteins that are not necessarily known biological targets of the compounds. Applying a carefully selected panel of proteins that can model the possible interactions a molecule can exert when administered to a human body, holds out a promise of biological activity prediction. This review aims to summarize a number of such bioactivity profiling-based approaches set up recently and present their application areas within the drug discovery field.

Identification of PPARγ ligands with One-dimensional Drug Profile Matching.

INTRODUCTION: Computational molecular database screening helps to decrease the time and resources needed for drug development. Reintroduction of generic drugs by second medical use patents also contributes to cheaper and faster drug development processes. We screened, in silico, the Food and Drug Administration-approved generic drug database by means of the One-dimensional Drug Profile Matching (oDPM) method in order to find potential peroxisome proliferator-activated receptor gamma (PPARγ) agonists. The PPARγ action of the selected generics was also investigated by in vitro and in vivo experiments. MATERIALS AND METHODS: The in silico oDPM method was used to determine the binding potency of 1,255 generics to 149 proteins collected. In vitro PPARγ activation was determined by measuring fatty acid-binding protein 4/adipocyte protein gene expression in a Mono Mac 6 cell line. The in vivo insulin sensitizing effect of the selected compound (nitazoxanide; 50-200 mg/kg/day over 8 days; n = 8) was established in type 2 diabetic rats by hyperinsulinemic euglycemic glucose clamping. RESULTS: After examining the closest neighbors of each of the reference set's members and counting their most abundant neighbors, ten generic drugs were selected with oDPM. Among them, four enhanced fatty acid-binding protein/adipocyte protein gene expression in the Mono Mac 6 cell line, but only bromfenac and nitazoxanide showed dose-dependent actions. Induction by nitazoxanide was higher than by bromfenac. Nitazoxanide lowered fasting blood glucose levels and improved insulin sensitivity in type 2 diabetic rats. CONCLUSION: We demonstrated that the oDPM method can predict previously unknown therapeutic effects of generic drugs. Nitazoxanide can be the prototype chemical structure of the new generation of insulin sensitizers.

Experimental confirmation of new drug-target interactions predicted by Drug Profile Matching.

We recently introduced Drug Profile Matching (DPM), a novel affinity fingerprinting-based in silico drug repositioning approach. DPM is able to quantitatively predict the complete effect profiles of compounds via probability scores. In the present work, in order to investigate the predictive power of DPM, three effect categories, namely, angiotensin-converting enzyme inhibitor, cyclooxygenase inhibitor, and dopamine agent, were selected and predictions were verified by literature analysis as well as experimentally. A total of 72% of the newly predicted and tested dopaminergic compounds were confirmed by tests on D1 and D2 expressing cell cultures. 33% and 23% of the ACE and COX inhibitory predictions were confirmed by in vitro tests, respectively. Dose-dependent inhibition curves were measured for seven drugs, and their inhibitory constants (Ki) were determined. Our study overall demonstrates that DPM is an effective approach to reveal novel drug-target pairs that may result in repositioning these drugs.

Thermodynamic analysis reveals structural rearrangement during the acylation step in human trypsin 4 on 4-methylumbelliferyl 4-guanidinobenzoate substrate analogue.

Human trypsin 4 is an unconventional serine protease that possesses an arginine at position 193 in place of the highly conserved glycine. Although this single amino acid substitution does not affect steady-state activity on small synthetic substrates, it has dramatic effects on zymogen activation, interaction with canonical inhibitors, and substrate specificity toward macromolecular substrates. To study the effect of a non-glycine residue at position 193 on the mechanism of the individual enzymatic reaction steps, we expressed wild type human trypsin 4 and its R193G mutant. 4-Methylumbelliferyl 4-guanidinobenzoate has been chosen as a substrate analogue, where deacylation is rate-limiting, and transient kinetic methods were used to monitor the reactions. This experimental system allows for the separation of the individual reaction steps during substrate hydrolysis and the determination of their rate constants dependably. We suggest a refined model for the reaction mechanism, in which acylation is preceded by the reversible formation of the first tetrahedral intermediate. Furthermore, the thermodynamics of these steps were also investigated. The formation of the first tetrahedral intermediate is highly exothermic and accompanied by a large entropy decrease for the wild type enzyme, whereas the signs of the enthalpy and entropy changes are opposite and smaller for the R193G mutant. This difference in the energetic profiles indicates much more extended structural and/or dynamic rearrangements in the equilibrium step of the first tetrahedral intermediate formation in wild type human trypsin 4 than in the R193G mutant enzyme, which may contribute to the biological function of this protease.

Enzyme kinetics above denaturation temperature: a temperature-jump/stopped-flow apparatus.

We constructed a "temperature-jump/stopped-flow" apparatus that allows us to study fast enzyme reactions at extremely high temperatures. This apparatus is a redesigned stopped-flow which is capable of mixing the reactants on a submillisecond timescale concomitant with a temperature-jump even as large as 60 degrees C. We show that enzyme reactions that are faster than the denaturation process can be investigated above denaturation temperatures. In addition, the temperature-jump/stopped-flow enables us to investigate at physiological temperature the mechanisms of many human enzymes, which was impossible until now because of their heat instability. Furthermore, this technique is extremely useful in studying the progress of heat-induced protein unfolding. The temperature-jump/stopped-flow method combined with the application of structure-specific fluorescence signals provides novel opportunities to study the stability of certain regions of enzymes and identify the unfolding-initiating regions of proteins. The temperature-jump/stopped-flow technique may become a breakthrough in exploring new features of enzymes and the mechanism of unfolding processes.