Delta Informatika Inc.

Budapest, Hungary

Delta Informatika Inc.

Budapest, Hungary

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Rauscher A.A.,Eötvös Loránd University | Simon Z.,Eötvös Loránd University | Simon Z.,Delta Informatika Inc. | Szollosi G.J.,Eötvös Loránd University | And 3 more authors.
FASEB Journal | Year: 2011

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. © FASEB.


Simon Z.,Eötvös Loránd University | Simon Z.,Delta Informatika Inc. | Vigh-Smeller M.,Eötvös Loránd University | Peragovics A.,Eötvös Loránd University | And 10 more authors.
BMC Structural Biology | Year: 2010

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. © 2010 Simon et al; licensee BioMed Central Ltd.


Simon Z.,Eötvös Loránd University | Simon Z.,Delta Informatika Inc. | Peragovics A.,Eötvös Loránd University | Peragovics A.,Delta Informatika Inc. | And 12 more authors.
Journal of Chemical Information and Modeling | Year: 2012

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. © 2011 American Chemical Society.


Vegner L.,Eötvös Loránd University | Peragovics A.,Eötvös Loránd University | Tombor L.,Semmelweis University | Jelinek B.,Eötvös Loránd University | And 8 more authors.
Journal of Medicinal Chemistry | Year: 2013

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. © 2013 American Chemical Society.

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