Biomedical subjects

G Mlinsek

Publications and source records attributed to G Mlinsek.

3 recordsLinked to original sources

PubMed2001

Design and structure-activity relationship of thrombin inhibitors with an azaphenylalanine scaffold: potency and selectivity enhancements via P2 optimization.

Theoretical and structural studies followed by the directed synthesis and in vitro biological tests lead us to novel noncovalent thrombin pseudopeptide inhibitors. We have incorporated an azapeptide scaffold into the central part of the classical tripeptide D-Phe-Pro-Arg inhibitor structure thus eliminating one stereogenic center from the molecule. A series of compounds has been designed to optimize the occupancy of the S2 pocket of thrombin. Increased hydrophobicity at P2 provides an enhanced fit into this active site S2 pocket. In the present paper, we also report on the structure of these inhibitors in solution and conformational analysis of inhibitors in the active site in order to asses the consequences of the replacement of the central alpha-CH by a nitrogen functionality. In vitro biological testing of the designed inhibitors shows that elimination of R, S stereoisomerism and restriction of conformational freedom influences the binding of inhibitors in a favorable fashion.

Antithrombins↗

PubMedDate not supplied

Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

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PubMedDate not supplied

Enzyme binding selectivity prediction: alpha-thrombin vs trypsin inhibition.

In the present work we explore the possibility of an in-depth computational analysis of available experimental X-ray structures in the specific case of a series of alpha-thrombin and trypsin complexes with their respective inhibitors for the development of a novel scoring function based on molecular electrostatic potential computed at the contact surface in the enzyme-inhibitor molecular complex. We subsequently employ the chemometrical approach to determine which are the interactions in the large volume of data that determine the resulting experimental binding constant between ligand and receptor. The results of the model evaluated with molecules in the independent validation set show that a reasonable average error of 1.30 log units of the difference between experimental and calculated binding constants was achieved in the system thrombin-trypsin, which is comparable with those of methods from the literature. Furthermore, by a careful preparation of the Kohonen top layer in the artificial neural network approach that is normally perceived as a "black box device", we have been able to follow the implications of the structure of the inhibitor-enzyme complex for the inhibitor's binding constant. The method appears to be suitable for evaluation of selectivity in structurally similar enzymatic systems, which is currently an important problem in drug design.

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