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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.26997
|2 doi
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|a pubmed24n1529.xml
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|a (DE-627)NLM347062644
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|a (NLM)36190786
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Zhou, Yingsheng
|e verfasserin
|4 aut
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|a Toward more potent imidazopyridine inhibitors of Candida albicans Bdf1
|b Modeling the role of structural waters in selective ligand binding
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 14.11.2022
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|a Date Revised 10.09.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
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|a Novel agents to treat invasive fungal infections are urgently needed because the small number of established targets in pathogenic fungi makes the existing drug repertoire particularly vulnerable to the emergence of resistant strains. Recently, we reported that Candida albicans Bdf1, a bromodomain and extra-terminal domain (BET) bromodomain with paired acetyl-lysine (AcK) binding sites (BD1 and BD2) is essential for fungal cell growth and that an imidazopyridine (1) binds to BD2 with selectivity versus both BD1 and human BET bromodomains. Bromodomain binding pockets contain a conserved array of structural waters. Molecular dynamics simulations now reveal that one water molecule is less tightly bound to BD2 than to BD1, explaining the site selectivity of 1. This insight is useful in the performance of ligand docking studies to guide design of more effective Bdf1 inhibitors, as illustrated by the design of 10 new imidazopyridine BD2 ligands 1a-j, for which experimental binding and site selectivity data are presented
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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|a Research Support, Non-U.S. Gov't
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|a BET inhibition
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|a Candida albicans
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|a antifungal drugs
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|a bromodomains
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|a molecular dynamics
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|a receptor-binding model
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|a structural water
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|a imidazopyridine
|2 NLM
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|a Ligands
|2 NLM
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|a Transcription Factors
|2 NLM
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|a Overhulse, Justin M
|e verfasserin
|4 aut
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|a Dupper, Nathan J
|e verfasserin
|4 aut
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| 700 |
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|a Guo, Yanchun
|e verfasserin
|4 aut
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|a Kashemirov, Boris A
|e verfasserin
|4 aut
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|a Wei, Kaiyao
|e verfasserin
|4 aut
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|a Govin, Jérôme
|e verfasserin
|4 aut
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|a Petosa, Carlo
|e verfasserin
|4 aut
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|a McKenna, Charles E
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 43(2022), 32 vom: 15. Dez., Seite 2121-2130
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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|g volume:43
|g year:2022
|g number:32
|g day:15
|g month:12
|g pages:2121-2130
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|u http://dx.doi.org/10.1002/jcc.26997
|3 Volltext
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