Sequential Proton Loss Electron Transfer in Deactivation of Iron(IV) Binding Protein by Tyrosine Based Food Components

2017 ◽  
Vol 65 (30) ◽  
pp. 6195-6210 ◽  
Author(s):  
Ning Tang ◽  
Leif H. Skibsted
Keyword(s):  
Electron Transfer ◽  
Binding Protein ◽  
Food Components ◽  
Proton Loss

scholarly journals Non-Linear Quantitative Structure–Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants

2019 ◽  
Vol 20 (9) ◽  
pp. 2328 ◽  
Author(s):  
Petar Žuvela ◽  
Jonathan David ◽  
Xin Yang ◽  
Dejian Huang ◽  
Ming Wah Wong
Keyword(s):  
Electron Transfer ◽  
Antioxidant Activities ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Non Linear ◽  
Quantitative Structure Activity Relationships ◽  
Mean Square Errors ◽  
Proton Loss

In this work, we developed quantitative structure–activity relationships (QSAR) models for prediction of oxygen radical absorbance capacity (ORAC) of flavonoids. Both linear (partial least squares—PLS) and non-linear models (artificial neural networks—ANNs) were built using parameters of two well-established antioxidant activity mechanisms, namely, the hydrogen atom transfer (HAT) mechanism defined with the minimum bond dissociation enthalpy, and the sequential proton-loss electron transfer (SPLET) mechanism defined with proton affinity and electron transfer enthalpy. Due to pronounced solvent effects within the ORAC assay, the hydration energy was also considered. The four-parameter PLS-QSAR model yielded relatively high root mean square errors (RMSECV = 0.783, RMSEE = 0.668, RMSEP = 0.900). Conversely, the ANN-QSAR model yielded considerably lower errors (RMSEE = 0.180 ± 0.059, RMSEP1 = 0.164 ± 0.128, and RMSEP2 = 0.151 ± 0.114) due to the inherent non-linear relationships between molecular structures of flavonoids and ORAC values. Five-fold cross-validation was found to be unsuitable for the internal validation of the ANN-QSAR model with a high RMSECV of 0.999 ± 0.253; which is due to limited sample size where resampling with replacement is a considerably better alternative. Chemical domains of applicability were defined for both models confirming their reliability and robustness. Based on the PLS coefficients and partial derivatives, both models were interpreted in terms of the HAT and SPLET mechanisms. Theoretical computations based on density functional theory at ωb97XD/6-311++G(d,p) level of theory were also carried out to further shed light on the plausible mechanism of anti-peroxy radical activity. Calculated energetics for simplified models (genistein and quercetin) with peroxyl radical derived from 2,2′-azobis (2-amidino-propane) dihydrochloride suggested that both SPLET and single electron transfer followed by proton loss (SETPL) mechanisms are competitive and more favorable than HAT in aqueous medium. The finding is in good accord with the ANN-based QSAR modelling results. Finally, the strongly predictive ANN-QSAR model was used to predict antioxidant activities for a series of 115 flavonoids designed combinatorially with flavone as a template. Structural trends were analyzed, and general guidelines for synthesis of new flavonoid derivatives with potentially potent antioxidant activities were given.

Conversion of an Electron-Transfer Protein into an Oxygen Binding Protein:  The Axial Cytochromeb5Mutant with an Unusually High O2Affinity

2000 ◽  
Vol 122 (46) ◽  
pp. 11535-11536 ◽  
Author(s):  
Masaki Ihara ◽  
Masato Shintaku ◽  
Satoshi Takahashi ◽  
Koichiro Ishimori ◽  
Isao Morishima
Keyword(s):  
Electron Transfer ◽  
Binding Protein ◽  
Oxygen Binding ◽  
Transfer Protein ◽  
Electron Transfer Protein
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