Unique action of a modified weakly acidic uncoupler without an acidic group, methylated SF 6847, as an inhibitor of oxidative phosphorylation with no uncoupling activity: Possible identity of uncoupler binding protein

Terada, Hiroshi; Fukui, Yousuke; Shinohara, Yasuo; Ju‐ichi, Motoharu

doi:10.1016/0005-2728(88)90070-9

Cited by 25 publications

(9 citation statements)

References 32 publications

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“…Structure‐toxicity studies [3,26–30] have indicated that potency of WARUs is typically depicted as a linear function of hydrophobicity measured as log K ow and electron‐withdrawing capability is depicted by the acid dissociation constant, pK a . Substitution of the acid‐dissociable moiety of a WARU with a nonacid‐dissociable substituent results in the loss of decoupling action [31]. Although high hydrophobicity and strong electron‐withdrawing capability are the principal physiochemical properties for uncoupling, the proportion of contribution of these attributes to decoupling is compound dependent.…”

Section: Discussionmentioning

confidence: 99%

Quantitative structure‐activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

Schultz

Cronin

1997

Enviro Toxic and Chemistry

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Acute toxicity values (5‐ and 30‐min Vibrio fisheri 50% luminescence inhibition) of 16 organic compounds thought to elicit their response via the weak acid respiratory uncoupling mechanism of toxic action were secured from the literature. Regression analysis of toxicities revealed that a measured 5‐min V. fisheri potency value can be used as a surrogate for the 30‐min value. Regression analysis of toxicity (30‐min for potency [log pT 30−1]) versus hydrophobicity, measured as the 1‐octanol/water partition coefficient (log Kow), was used to formulate a quantitative structure‐activity relationship (QSAR). The equation log pT 30−1 = 0.489(log Kow) + 0.126 was found to be a highly predictive model (r2 adj. = 0.848). This V. fisheri QSAR is statistically similar to QSARs generated from weak acid uncoupler potency data for Pimephales promelas survivability and Tetrahymena pyriformis population growth impairment. This work, therefore, suggests that the weak acid respiratory uncoupling mechanism of toxic action is present in V. fisheri, and as such is not restricted to mitochondria‐containing organisms.

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Section: Discussionmentioning

confidence: 99%

Quantitative structure‐activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

Schultz

Cronin

1997

Enviro Toxic and Chemistry

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“…Structure-toxicity studies [3,[26][27][28][29][30] have indicated that potency of WARUs is typically depicted as a linear function of hydrophobicity measured as log K ow and electron-withdrawing capability is depicted by the acid dissociation constant, pK a . Substitution of the acid-dissociable moiety of a WARU with a nonacid-dissociable substituent results in the loss of decoupling action [31]. Although high hydrophobicity and strong electron-withdrawing capability are the principal physiochemical properties for uncoupling, the proportion of contribution of these attributes to decoupling is compound dependent.…”

Section: Respiratory Uncoupling Qsarsmentioning

confidence: 99%

Quantitative Structure–activity Relationships for Weak Acid Respiratory Uncouplers to Vibrio Fisheri

Schultz¹,

Cronin²

1997

Environ Toxicol Chem

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Abstract-Acute toxicity values (5-and 30-min Vibrio fisheri 50% luminescence inhibition) of 16 organic compounds thought to elicit their response via the weak acid respiratory uncoupling mechanism of toxic action were secured from the literature. Regression analysis of toxicities revealed that a measured 5-min V. fisheri potency value can be used as a surrogate for the 30-min value. Regression analysis of toxicity (30-min for potency [log p ]) versus hydrophobicity, measured as the 1-octanol/water partition Ϫ1 T 30 coefficient (log K ow ), was used to formulate a quantitative structure-activity relationship (QSAR). The equation log p ϭ 0.489(log Ϫ1T 30 K ow ) ϩ 0.126 was found to be a highly predictive model (r 2 adj. ϭ 0.848). This V. fisheri QSAR is statistically similar to QSARs generated from weak acid uncoupler potency data for Pimephales promelas survivability and Tetrahymena pyriformis population growth impairment. This work, therefore, suggests that the weak acid respiratory uncoupling mechanism of toxic action is present in V. fisheri, and as such is not restricted to mitochondria-containing organisms.

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“…Visualizing the sensitivity map for SR-MMP toxicity prediction with BayesGrad. (a) Chemical structure of Tyrphostin 9 and our model highlighted the phenolic OH (acid-dissociable) group, which is confirmed to be essential for uncoupling[15]. (b) O-methylated derivative of Tyrphostin 9 does not induce uncoupling.…”

mentioning

confidence: 74%

“…3 (a)) is a tyrosine kinase inhibitor and is known to be a potent uncoupler of oxidative phosphorylation, which has a strong influence on the mitochondrial membrane potential (SR-MMP ). Terada et al [15] examined the effect of the mitochondrial function of the acid-dissociable group using Tyrphostin 9 and a derivative, modified by methylation of its phenolic OH group. They confirmed that the acid-dissociable group is essential for uncoupling.…”

Section: Visualization On Tox21 Actual Datamentioning

confidence: 99%

BayesGrad: Explaining Predictions of Graph Convolutional Networks

Akita

Nakago

Komatsu

et al. 2018

Lecture Notes in Computer Science

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Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predictions of graph convolutional networks?" A possible approach to answer this question is to visualize evidence substructures responsible for the predictions. For chemical property prediction tasks, the sample size of the training data is often small and/or a label imbalance problem occurs, where a few samples belong to a single class and the majority of samples belong to the other classes. This can lead to uncertainty related to the learned parameters of the machine learning model. To address this uncertainty, we propose BayesGrad, utilizing the Bayesian predictive distribution, to define the importance of each node in an input graph, which is computed efficiently using the dropout technique. We demonstrate that BayesGrad successfully visualizes the substructures responsible for the label prediction in the artificial experiment, even when the sample size is small. Furthermore, we use a real dataset to evaluate the effectiveness of the visualization. The basic idea of BayesGrad is not limited to graph-structured data and can be applied to other data types.

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Unique action of a modified weakly acidic uncoupler without an acidic group, methylated SF 6847, as an inhibitor of oxidative phosphorylation with no uncoupling activity: Possible identity of uncoupler binding protein

Cited by 25 publications

References 32 publications

Quantitative structure‐activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

Quantitative structure‐activity relationships for weak acid respiratory uncouplers to Vibrio fisheri

Quantitative Structure–activity Relationships for Weak Acid Respiratory Uncouplers to Vibrio Fisheri

BayesGrad: Explaining Predictions of Graph Convolutional Networks

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