scholarly journals Thermodynamics of Natural and Synthetic Inhibitor Binding to Human Hsp90

Author(s):  
Vilma Petrikaite ◽  
Daumantas Matulis
2021 ◽  
Author(s):  
Manuela Oliverio ◽  
Monica Nardi ◽  
Maria Luisa Di Gioia ◽  
Paola Costanzo ◽  
Sonia Bonacci ◽  
...  

Semi-synthesis is an effective strategy to obtain both natural and synthetic analogues of the olive secoiridoids, starting from easy accessible natural compounds.


Planta Medica ◽  
2011 ◽  
Vol 77 (12) ◽  
Author(s):  
F Namjooyan ◽  
H Moosavi ◽  
A Taherian

Planta Medica ◽  
2007 ◽  
Vol 73 (09) ◽  
Author(s):  
E Xingi ◽  
D Smirlis ◽  
S Bisti ◽  
V Myrianthopoulos ◽  
P Magiatis ◽  
...  

1963 ◽  
Vol 43 (4) ◽  
pp. 561-570 ◽  
Author(s):  
Gösta Bengtsson ◽  
Sven Ullberg

ABSTRACT The distribution in mice of 14C- and 3H-diethylstilboestrol has been investigated autoradiographically. The results have been compared with those which have been previously reported for natural oestrogens. Many similarities have been demonstrated between the synthetic and natural compounds. Thus a specific accumulation has been observed in the endometrium, the granulosa layer of large ovarian follicles, the adrenal cortex. the interstitial tissue of the testes, and the hypophysis. Natural and synthetic oestrogens differ widely concerning the penetration into and the distribution within the foetus.


1981 ◽  
Vol 17 (8) ◽  
pp. 528-541 ◽  
Author(s):  
Robert Crowningshield ◽  
Kurt Nassau

2019 ◽  
Author(s):  
Andrea N. Bootsma ◽  
Analise C. Doney ◽  
Steven Wheeler

<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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