In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear...In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.展开更多
The bioactive constituents found in natural products(NPs)are crucial in protein-ligand interactions and drug discovery.However,it is difficult to identify ligand molecules from complex NPs that specifically bind to ta...The bioactive constituents found in natural products(NPs)are crucial in protein-ligand interactions and drug discovery.However,it is difficult to identify ligand molecules from complex NPs that specifically bind to target protein,which often requires time-consuming and labor-intensive processes such as isolation and enrichment.To address this issue,in this study we developed a method that combines ultra-high performance liquid chromatography-electrospray ionization-mass spectrometry(UHPLCESI-MS)with molecular dynamics(MD)simulation to identify and observe,rapidly and efficiently,the bioactive components in NPs that bind to specific protein target.In this method,a specific protein target was introduced online using a three-way valve to form a protein-ligand complex.The complex was then detected in real time using high-resolution MS to identify potential ligands.Based on our method,only 10 molecules from green tea(a representative natural product),including the commonly reported epigallocatechin gallate(EGCG)and epicatechin gallate(ECG),as well as the previously unreported eepicatechin(4β→8)-epigallocatechin 3-O-gallate(EC-EGCG)and eepiafzelechin 3-O-gallate-(4β→8)-epigallocatechin 3-O-gallate(EFG-EGCG),were screened out,which could form complexes with Aβ_(1-42)(a representative protein target),and could be potential ligands of Aβ_(1-42).Among of them,EC-EGCG demonstrated the highest binding free energy with Aβ_(1-42)(−68.54±3.82 kcal/mol).On the other side,even though the caffeine had the highest signal among green tea extracts,it was not observed to form a complex with Aβ_(1-42).Compared to other methods such as affinity selection mass spectrometry(ASMS)and native MS,our method is easy to operate and interpret the data.Undoubtedly,it provides a new methodology for potential drug discovery in NPs,and will accelerate the research on screening ligands for specific proteins from complex NPs.展开更多
Two polysaccharide-based chiral stationary phase columns were evaluated to improve the previous partial chiral peak separation to a baseline-resolved separation of the INGREZZA® drug substance and its diastereome...Two polysaccharide-based chiral stationary phase columns were evaluated to improve the previous partial chiral peak separation to a baseline-resolved separation of the INGREZZA® drug substance and its diastereomers. Moreover, the tailing factor (Tf) variation was studied to investigate chiral column degradation and regeneration and to optimize chiral column performance and efficiency.展开更多
基金Supported by the National Natural Science Foundation of China (No. 20306023).
文摘In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.
基金supported by the National Key R&D Program of China(No.2018YFA0800900).
文摘The bioactive constituents found in natural products(NPs)are crucial in protein-ligand interactions and drug discovery.However,it is difficult to identify ligand molecules from complex NPs that specifically bind to target protein,which often requires time-consuming and labor-intensive processes such as isolation and enrichment.To address this issue,in this study we developed a method that combines ultra-high performance liquid chromatography-electrospray ionization-mass spectrometry(UHPLCESI-MS)with molecular dynamics(MD)simulation to identify and observe,rapidly and efficiently,the bioactive components in NPs that bind to specific protein target.In this method,a specific protein target was introduced online using a three-way valve to form a protein-ligand complex.The complex was then detected in real time using high-resolution MS to identify potential ligands.Based on our method,only 10 molecules from green tea(a representative natural product),including the commonly reported epigallocatechin gallate(EGCG)and epicatechin gallate(ECG),as well as the previously unreported eepicatechin(4β→8)-epigallocatechin 3-O-gallate(EC-EGCG)and eepiafzelechin 3-O-gallate-(4β→8)-epigallocatechin 3-O-gallate(EFG-EGCG),were screened out,which could form complexes with Aβ_(1-42)(a representative protein target),and could be potential ligands of Aβ_(1-42).Among of them,EC-EGCG demonstrated the highest binding free energy with Aβ_(1-42)(−68.54±3.82 kcal/mol).On the other side,even though the caffeine had the highest signal among green tea extracts,it was not observed to form a complex with Aβ_(1-42).Compared to other methods such as affinity selection mass spectrometry(ASMS)and native MS,our method is easy to operate and interpret the data.Undoubtedly,it provides a new methodology for potential drug discovery in NPs,and will accelerate the research on screening ligands for specific proteins from complex NPs.
文摘Two polysaccharide-based chiral stationary phase columns were evaluated to improve the previous partial chiral peak separation to a baseline-resolved separation of the INGREZZA® drug substance and its diastereomers. Moreover, the tailing factor (Tf) variation was studied to investigate chiral column degradation and regeneration and to optimize chiral column performance and efficiency.
