19 research outputs found

    Structural Determinants for the Inhibitory Ligands of Orotidine-5'-Monophosphate Decarboxylase

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    In recent years, orotidine-5'-monophosphate decarboxylase (ODCase) has gained renewed attention as a drug target. As a part of continuing efforts to design novel inhibitors of ODCase, we undertook a comprehensive study of potent, structurally diverse ligands of ODCase and analyzed their structural interactions in the active site of ODCase. These ligands comprise of pyrazole or pyrimidine nucleotides including the mononucleotide derivatives of pyrazofurin, barbiturate ribonucleoside, and 5-cyanouridine, as well as, in a computational approach, 1,4-dihydropyridine-based non-nucleoside inhibitors such as nifedipine and nimodipine. All these ligands bind in the active site of ODCase exhibiting distinct interactions paving the way to design novel inhibitors against this interesting enzyme. We propose an empirical model for the ligand structure for rational modifications in new drug design and potentially new lead structures

    Alkynes and Azides: Not Just for Click Reactions.

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    We recently reported two unexplored reactivities of alkynes and azides. The first method reacts nucleophilic alkynes and electrophilic azides to synthesize sulfonyl-substituted 1,5-disubstituted-1,2,3-triazoles. The second method reacts electrophilic alkynes with nucleophilic azides to form alkynyl-azides that immediately extrude dinitrogen to form cyanocarbenes which were trapped by O-H insertion, sulfoxide complexation, and cyclopropanation. The design and discovery of these reactions, along with key observations, is discussed herein

    Exploring the reactivity of 1,5-disubstituted sulfonyl-triazoles: Thermolysis and Rh(II)-catalyzed synthesis of a-sulfonyl nitriles

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    The reactivity of a series of 1,5-disubstituted sulfonyl-triazoles was explored using either thermolytic or metal-catalyzed conditions. Both the thermolysis and the Rh(II)-catalyzed reactions led to the synthesis of a-sulfonyl-nitriles, which presumably occurred through a carbene or carbenoid mechanism. The reactivity of the carbenes and carbenoids resulting from the loss of dinitrogen from the 1,5-disubstituted sulfonyl-triazoles were different from those of the previously explored 1,4-disubstituted sulfonyl-triazoles. It was observed by NMR that the Rh(II)-catalyst coordinates strongly but reversibly with the 1,5-disubstituted sulfonyl-triazoles. Other catalysts, including both Brønsted and Lewis acids, were found to catalyze this transformation, although less efficiently compared to neat thermolysis or Rh(II)-catalyzed conditions. These data illustrate both the unique nature of 1,5-disubstituted sulfonyl-triazoles and potential future avenues for their utilization

    Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

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    Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

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    The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is O(249) computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of 0.89 and 0.88 and Jaccard Index of 0.80 and 0.79, respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ≈0.02 s, which made it highly suitable to be used in clinical practice
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