Change in Volume Upon Inhibitor Binding to Carbonic Anhydrases by Fluorescent Pressure Shift Assay

2019 ◽  
pp. 97-106
Author(s):  
Gediminas Skvarnavičius ◽  
Daumantas Matulis ◽  
Vytautas Petrauskas
Keyword(s):  
Carbonic Anhydrases ◽  
Inhibitor Binding ◽  
Pressure Shift

Inhibitor Binding to Carbonic Anhydrases by Isothermal Titration Calorimetry

2019 ◽  
pp. 79-95
Author(s):  
Vaida Paketurytė ◽  
Asta Zubrienė ◽  
Wen-Yih Chen ◽  
Sandro Keller ◽  
Margarida Bastos ◽  
...  
Keyword(s):  
Isothermal Titration Calorimetry ◽  
Titration Calorimetry ◽  
Carbonic Anhydrases ◽  
Inhibitor Binding

In Silico Modeling of Inhibitor Binding to Carbonic Anhydrases

2019 ◽  
pp. 215-232
Author(s):  
Visvaldas Kairys ◽  
Kliment Olechnovič ◽  
Vytautas Raškevičius ◽  
Daumantas Matulis
Keyword(s):  
In Silico ◽  
Carbonic Anhydrases ◽  
Inhibitor Binding ◽  
In Silico Modeling

scholarly journals Intrinsic thermodynamics of sulfonamide inhibitor binding to human carbonic anhydrases I and II

2014 ◽  
Vol 30 (2) ◽  
pp. 204-211 ◽  
Author(s):  
Vaida Morkūnaitė ◽  
Joana Gylytė ◽  
Asta Zubrienė ◽  
Lina Baranauskienė ◽  
Miglė Kišonaitė ◽  
...  
Keyword(s):  
Carbonic Anhydrases ◽  
Inhibitor Binding

Melatonin modifies tumor hypoxia and metabolism by inhibiting HIF-1α and energy metabolic pathway in the in vitro and in vivo models of breast cancer

2019 ◽  
Vol 2 (4) ◽  
pp. 83-98 ◽  
Author(s):  
André De Lima Mota ◽  
Bruna Vitorasso Jardim-Perassi ◽  
Tialfi Bergamin De Castro ◽  
Jucimara Colombo ◽  
Nathália Martins Sonehara ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Glucose Metabolism ◽  
Tumor Growth ◽  
Tumor Cells ◽  
Carbonic Anhydrases ◽  
In Vivo Models ◽  
Ca Ix ◽  
Gene And Protein Expression

Breast cancer is the most common cancer among women and has a high mortality rate. Adverse conditions in the tumor microenvironment, such as hypoxia and acidosis, may exert selective pressure on the tumor, selecting subpopulations of tumor cells with advantages for survival in this environment. In this context, therapeutic agents that can modify these conditions, and consequently the intratumoral heterogeneity need to be explored. Melatonin, in addition to its physiological effects, exhibits important anti-tumor actions which may associate with modification of hypoxia and Warburg effect. In this study, we have evaluated the action of melatonin on tumor growth and tumor metabolism by different markers of hypoxia and glucose metabolism (HIF-1α, glucose transporters GLUT1 and GLUT3 and carbonic anhydrases CA-IX and CA-XII) in triple negative breast cancer model. In an in vitro study, gene and protein expressions of these markers were evaluated by quantitative real-time PCR and immunocytochemistry, respectively. The effects of melatonin were also tested in a MDA-MB-231 xenograft animal model. Results showed that melatonin treatment reduced the viability of MDA-MB-231 cells and tumor growth in Balb/c nude mice (p <0.05). The treatment significantly decreased HIF-1α gene and protein expression concomitantly with the expression of GLUT1, GLUT3, CA-IX and CA-XII (p <0.05). These results strongly suggest that melatonin down-regulates HIF-1α expression and regulates glucose metabolism in breast tumor cells, therefore, controlling hypoxia and tumor progression. 

Predicting the Strength of Stacking Interactions Between Heterocycles and Aromatic Amino Acid Side Chains

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Analise C. Doney ◽  
Steven Wheeler
Keyword(s):  
Amino Acid ◽  
Binding Sites ◽  
Aromatic Amino Acid ◽  
Aromatic Amino Acids ◽  
Biologically Active ◽  
Side Chains ◽  
Stacking Interactions ◽  
Inhibitor Binding ◽  
Protein Binding Sites ◽  
Amino Acid Side Chains

<p>Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. Based on rigorous <i>ab initio</i> data, we have devised a simple predictive model of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. This model provides rapid predictions of the stacking ability of a given heterocycle based on readily-computed heterocycle descriptors. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow simple predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual model for understanding stacking interactions in protein binding sites and optimizing inhibitor binding in drug design.</p>

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