Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:3.4.21.5 (thrombin)
 33,306 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Surface immobilization of the thrombin inhibitor r-hirudin was carried out on two different polymers. Linkage to poly(urethane-graft-acrylic acid) (PAC/PU) was done via carboxylic acid groups, using a water soluble carbodimide, while the immobilization on a modified poly[(ethene-co-vinyl acetate)-graft-vinyl chloride] (PVC/EVA) was achieved via the alcohol groups of the polymer using HDI as spacer. Direct immobilization of r-hirudin leaded to a remarkable loss of thrombin activity. As proved by means of protein chemical analysis, loss of activity was due to a selective coupling via the N-terminal amino group of r-hirudin, which is essential for its thrombin activity. Based on these results we developed an immobilization method via an epsilon-amino group of r-hirudin preserving full biological activity of the r-hirudin coated surface.
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PMID:Immobilization of the thrombin inhibitor r-hirudin conserving its biological activity. 1534 28

A large number of methods are available for modeling quantitative structure-activity relationships (QSAR). We examine the predictive accuracy of several methods applied to data sets of inhibitors for angiotensin converting enzyme, acetylcholinesterase, benzodiazepine receptor, cyclooxygenase-2, dihydrofolate reductase, glycogen phosphorylase b, thermolysin, and thrombin. Descriptors calculated with CoMFA, CoMSIA, EVA, HQSAR, and traditional 2D and 2.5D descriptors were used for developing models with partial least squares (PLS). In addition, the genetic function approximation algorithm, genetic PLS, and back-propagation neural networks were used for deriving models from 2.5D descriptors (i.e., 2D descriptors and 3D descriptors calculated from CORINA structures and Gasteiger-Marsili charges). Predictive accuracy was assessed using designed test sets. It was found that HQSAR generally performs as well as CoMFA and CoMSIA; other descriptor sets performed less well. When 2.5D descriptors were used, only neural network ensembles were found to be similarly or more predictive than PLS models. In addition, we show that many cross-validation procedures yield similar estimates of the interpolative accuracy of methods. However, the lack of correspondence between cross-validated and test set predictive accuracy for four sets underscores the benefit of using designed test sets.
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PMID:A comparison of methods for modeling quantitative structure-activity relationships. 1548 90