3310-99-4Relevant articles and documents
Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica
Prado-Prado, Francisco,García-Mera, Xerardo,Abeijón, Paula,Alonso, Nerea,Caama?o, Olga,Yá?ez, Matilde,Gárate, Teresa,Mezo, Mercedes,González-Warleta, Marta,Mui?o, Laura,Ubeira, Florencio M.,González-Díaz, Humberto
, p. 1074 - 1094 (2011)
There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.
Burkholderia cepacia lipase is an excellent enzyme for the enantioselective hydrolysis of β-heteroaryl-β-amino esters
Tasnadi, Gabor,Forro, Eniko,Fueloep, Ferenc
experimental part, p. 1771 - 1777 (2009/12/28)
The enantioselective (E >200) lipase PS-catalysed hydrolysis of β-heteroaryl-β-amino esters is described. The reactions were performed with H2O (0.5 equiv) in either diisopropyl ether or tert-butyl methyl ether at 25 °C. The resulting β-heteroaryl-substituted β-amino acid enantiomers were formed in high enantiomeric excess (ee ≥ 97%) and in good yield (≥40%).
2-CYANOPYRROLES AND THEIR ANALOGUES AS DDP-IV INHIBITORS
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Page/Page column 33, (2010/02/08)
The present invention relates to therapeutically active and selective inhibitors of the enzyme DPP-IV having the formula I: (I) The invention furthermore relates to pharmaceutical compositions comprising the compounds and the use of such compounds for the manufacture of medicaments for treating diseases that are associated with proteins which are subject to inactivation by DPP-IV, such as type 2 diabetes and obesity.
