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.

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.

The crystal structure of a trypsin-like mutant chymotrypsin: the role of position 226 in the activity and specificity of S189D chymotrypsin.

The crystal structure of the S189D+A226G rat chymotrypsin-B mutant has been determined at 2.2 angstroms resolution. This mutant is the most trypsin-like mutant so far in the line of chymotrypsin-to-trypsin conversions, aiming for a more complete understanding of the structural basis of substrate specificity in pancreatic serine proteases. A226G caused significant rearrangements relative to S189D chymotrypsin, allowing an internal conformation of Asp189 which is close to that in trypsin. Serious distortions remain, however, in the activation domain, including zymogen-like features. The pH-profile of activity suggests that the conformation of the S1-site of the mutant is influenced also by the P1 residue of the substrate.

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.

Ala226 to Gly and Ser189 to Asp mutations convert rat chymotrypsin B to a trypsin-like protease.

In a previous successful attempt to convert trypsin to a chymotrypsin-like protease, 15 residues of trypsin were replaced with the corresponding ones in chymotrypsin. This suggests a complex mechanism of substrate recognition instead of a relatively simple one that only involves three sites, residues 189, 216 and 226. However, both trypsin-->elastase and chymotrypsin-->trypsin conversion experiments carried out according to the complex model resulted in non-specific proteases with low catalytic activity. Chymotrypsin used in the latter studies was of B-type, containing an Ala residue at position 226. Trypsins, however, contain a conserved Gly at this site. The substantially decreased trypsin-like activity of the G226A trypsin mutant also suggests a specific role for this site in substrate binding. Here we investigate the role of site 226 by introducing the A226G substitution into chymotrypsin-->trypsin mutants which were constructed according to both the simple (S189D mutant) and the complex model (S(1) mutant) of specificity determination. The kinetic parameters show that the A226G substitution in the S(1) mutant increased the chymotrypsin-like activity, while the trypsin-like activity did not change. In contrast, this substitution in the S189D chymotrypsin mutant resulted in a 100-fold increase in trypsin-like activity and a trypsin-like specificity profile as tested on a competing oligopeptide substrate library. Additionally, the S189D+A226G mutant is the first trypsin-like chymotrypsin that undergoes autoactivation, an exclusive property of trypsinogen among pancreatic serine proteases.

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.

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.