Human serum albumin （HSA） is a plasma protein responsible for the binding and transport of fatty acids and a variety of exogenous chemicals such as drugs and environmental pollutants. Such binding plays a crucial ro...Human serum albumin （HSA） is a plasma protein responsible for the binding and transport of fatty acids and a variety of exogenous chemicals such as drugs and environmental pollutants. Such binding plays a crucial role in determining the ADME （absorption, distribution, metabolism, and excretion） and bioavailability of the pollutants. The binding interaction between HSA and acetic acid （C2）, octanoic acid （C8） and dodecanoic acid （C12） has been investigated by the combination of site-specific fluorescent probe, tryptophan intrinsic fluorescence and tyrosine electrochemistry. For the study of the fatty acid interaction with the two drug-binding sites on HSA, two fluorescent probes, dansylamide and dansyl-L-proline were employed in the displacement measurements. Intrinsic fluorescence of tryptophan in HSA was monitored upon addition of the fatty acids into HSA. Electrocatalyzed response of the tyrosine residues in HSA by a redox mediator was used to investigate the binding interaction. Qualitatively, observations from these three approaches were very similar. HSA did not show any change in the fluorescence and electrochemical experiments after mixing with C2, suggesting there is no significant interaction with the short-chain fatty acid. For C8, the measured signal dropped in a single-exponential mode, indicating an independent and non-cooperative binding. The calculated association constant and binding ratio were 3.1 × 10^6 L/mol and 1 with drug binding Site Ⅰ, 1.1 × 107 L/mol and 1 with Site Ⅱ, and 7.0× 0^4 L/mol and 4 with the tryptophan site, respectively. The measurements with C12 displayed multiple phases of fluorescence change, suggesting cooperativity and allosteric effect of the C12 binding. These results correlate well with those obtained by the established methods, and validate the new approach as a viable tool to study the interactions of environmental pollutants with biological molecules.展开更多
Objective To explore the anti-inflammatory phytoconstituents from various plant sources as tumour necrosis factor-α(TNF-α)-inhibitor,a mediator involved in the inflammatory disorder,by in silico molecular docking.Me...Objective To explore the anti-inflammatory phytoconstituents from various plant sources as tumour necrosis factor-α(TNF-α)-inhibitor,a mediator involved in the inflammatory disorder,by in silico molecular docking.Methods Based on previous findings,we performed the in silico assessment of anti-inflammatory phytoconstituents from different medicinal plants to understand their binding patterns against TNF-α(PDB ID:6OP0)using AutoDock Vina.Molecular docking was performed by setting a grid box(25×25×25)Åcentered at[-12.817×(-1.618)×19.009]A with 0.375A of grid spacing.Furthermore,Discovery Studio Client 2020 program was utilized to assess two-and three-dimensional(2D and 3D)hydrogen-bond interactions concerning an amino acid of target and ligand.Physicochemical properties were reported using the Lipinski’s rule and SwissADME database to support the in silico findings.Results From the selected medicinal plants,more than 200 phytocompounds were screened against TNF-α protein with binding scores in the range of -12.3 to -2.5 kcal/mol.Amongst them,emodin,aloe-emodin,pongamol,purpuritenin,semiglabrin,ellagic acid,imperatorin,α-tocopherol,and octanorcucurbitacin A showed good binding affinity as -10.6,-10.0,-10.5,-10.1,-11.2,-10.3,-10.1,-10.1,and -10.0 kcal/mol,respectively.Also,the absorption,distribution,metabolism,excretion,and toxicology(ADMET)profiles were well within acceptable limits.Conclusion Based on our preliminary findings,we conclude that the selected phytoconstituents have the potential to be good anti-inflammatory candidates by inhibiting the TNF-α target.These compounds can be further optimized and validated as new therapeutic components to develop more effective and safe anti-inflammatory drugs.展开更多
Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Ab...Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.展开更多
基金supported by the National Basic Re-search Program of China (No. 2006CB403303)the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX2-YW-420-1)the National Natural Science Foundation of China (No. 20890112)
文摘Human serum albumin （HSA） is a plasma protein responsible for the binding and transport of fatty acids and a variety of exogenous chemicals such as drugs and environmental pollutants. Such binding plays a crucial role in determining the ADME （absorption, distribution, metabolism, and excretion） and bioavailability of the pollutants. The binding interaction between HSA and acetic acid （C2）, octanoic acid （C8） and dodecanoic acid （C12） has been investigated by the combination of site-specific fluorescent probe, tryptophan intrinsic fluorescence and tyrosine electrochemistry. For the study of the fatty acid interaction with the two drug-binding sites on HSA, two fluorescent probes, dansylamide and dansyl-L-proline were employed in the displacement measurements. Intrinsic fluorescence of tryptophan in HSA was monitored upon addition of the fatty acids into HSA. Electrocatalyzed response of the tyrosine residues in HSA by a redox mediator was used to investigate the binding interaction. Qualitatively, observations from these three approaches were very similar. HSA did not show any change in the fluorescence and electrochemical experiments after mixing with C2, suggesting there is no significant interaction with the short-chain fatty acid. For C8, the measured signal dropped in a single-exponential mode, indicating an independent and non-cooperative binding. The calculated association constant and binding ratio were 3.1 × 10^6 L/mol and 1 with drug binding Site Ⅰ, 1.1 × 107 L/mol and 1 with Site Ⅱ, and 7.0× 0^4 L/mol and 4 with the tryptophan site, respectively. The measurements with C12 displayed multiple phases of fluorescence change, suggesting cooperativity and allosteric effect of the C12 binding. These results correlate well with those obtained by the established methods, and validate the new approach as a viable tool to study the interactions of environmental pollutants with biological molecules.
文摘Objective To explore the anti-inflammatory phytoconstituents from various plant sources as tumour necrosis factor-α(TNF-α)-inhibitor,a mediator involved in the inflammatory disorder,by in silico molecular docking.Methods Based on previous findings,we performed the in silico assessment of anti-inflammatory phytoconstituents from different medicinal plants to understand their binding patterns against TNF-α(PDB ID:6OP0)using AutoDock Vina.Molecular docking was performed by setting a grid box(25×25×25)Åcentered at[-12.817×(-1.618)×19.009]A with 0.375A of grid spacing.Furthermore,Discovery Studio Client 2020 program was utilized to assess two-and three-dimensional(2D and 3D)hydrogen-bond interactions concerning an amino acid of target and ligand.Physicochemical properties were reported using the Lipinski’s rule and SwissADME database to support the in silico findings.Results From the selected medicinal plants,more than 200 phytocompounds were screened against TNF-α protein with binding scores in the range of -12.3 to -2.5 kcal/mol.Amongst them,emodin,aloe-emodin,pongamol,purpuritenin,semiglabrin,ellagic acid,imperatorin,α-tocopherol,and octanorcucurbitacin A showed good binding affinity as -10.6,-10.0,-10.5,-10.1,-11.2,-10.3,-10.1,-10.1,and -10.0 kcal/mol,respectively.Also,the absorption,distribution,metabolism,excretion,and toxicology(ADMET)profiles were well within acceptable limits.Conclusion Based on our preliminary findings,we conclude that the selected phytoconstituents have the potential to be good anti-inflammatory candidates by inhibiting the TNF-α target.These compounds can be further optimized and validated as new therapeutic components to develop more effective and safe anti-inflammatory drugs.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0082)
文摘Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.
