A secretory kinase complex regulates extracellular protein phosphorylation.

Although numerous extracellular phosphoproteins have been identified, the protein kinases within the secretory pathway have only recently been discovered, and their regulation is virtually unexplored. Fam20C is the physiological Golgi casein kinase, which phosphorylates many secreted proteins and is critical for proper biomineralization. Fam20A, a Fam20C paralog, is essential for enamel formation, but the biochemical function of Fam20A is unknown. Here we show that Fam20A potentiates Fam20C kinase activity and promotes the phosphorylation of enamel matrix proteins in vitro and in cells. Mechanistically, Fam20A is a pseudokinase that forms a functional complex with Fam20C, and this complex enhances extracellular protein phosphorylation within the secretory pathway. Our findings shed light on the molecular mechanism by which Fam20C and Fam20A collaborate to control enamel formation, and provide the first insight into the regulation of secretory pathway phosphorylation

Non-Competitive Peak Decay Analysis Of Drugprotein Dissociation By High-Performance Affinity Chromatography

The peak decay method is an affinity chromatographic technique that has been used to examine the dissociation of solutes from immobilized ligands in the presence of excess displacing agent. However, it can be difficult to find a displacing agent that does not interfere with detection of the eluting analyte. In this study, a non-competitive peak decay method was developed in which no displacing agent was required for analyte elution. This method was evaluated for the study of drug-protein interactions by using it along with high-performance affinity chromatography to measure the dissociation rate constants for R- and S-warfarin from columns containing immobilized human serum albumin (HSA). Several factors were considered in the optimization of this method, including the amount of applied analyte, the column size, and the flow rate. The dissociation rate constants for R- and S-warfarin from HSA were measured at several temperatures by this approach, giving values of 0.56 (± 0.01) and 0.66 (± 0.01) s−1 at pH 7.4 and 37°C. These results were in good agreement with previous values obtained by other methods. This approach is not limited to warfarin and HSA but could be employed in studying additional drug-protein interactions or other systems with weak-to-moderate binding

Non-Competitive Peak Decay Analysis Of Drugprotein Dissociation By High-Performance Affinity Chromatography

The peak decay method is an affinity chromatographic technique that has been used to examine the dissociation of solutes from immobilized ligands in the presence of excess displacing agent. However, it can be difficult to find a displacing agent that does not interfere with detection of the eluting analyte. In this study, a non-competitive peak decay method was developed in which no displacing agent was required for analyte elution. This method was evaluated for the study of drug-protein interactions by using it along with high-performance affinity chromatography to measure the dissociation rate constants for R- and S-warfarin from columns containing immobilized human serum albumin (HSA). Several factors were considered in the optimization of this method, including the amount of applied analyte, the column size, and the flow rate. The dissociation rate constants for R- and S-warfarin from HSA were measured at several temperatures by this approach, giving values of 0.56 (± 0.01) and 0.66 (± 0.01) s−1 at pH 7.4 and 37°C. These results were in good agreement with previous values obtained by other methods. This approach is not limited to warfarin and HSA but could be employed in studying additional drug-protein interactions or other systems with weak-to-moderate binding

Study on the extraction of dioscin by the ultrasonicassisted ethanol

With Dioscorea zingiberensis as row materials, and with the yield of diosgenin as assessment criteria, the effect on extraction yield of dioscin of frequency of ultrasonic, the period of ultrasonic and solidliquid ratio (D. zingiberensis : alcohol) was studied via orthogonal test. A new and unique method to accomplish this was by utilizing the technology of ultrasonic assisted ethanol extraction. The optimal processing parameters of this method were confirmed. The method was compared with solvent extraction process for the effect on extraction yield of dioscin. It was shown that the technology of ultrasonic assisted ethanol extraction which can significantly increase the extraction yield and extraction efficiency of dioscin. The ultrasonic did not destroy D. zingiberensis cell structure, but decreased the boundary layer thickness between D. zingiberensis (solid phase) and alcohol (medium), and accelerated cells inside and outside the material exchange. International rectifier (IR) further demonstrated that ultrasonic merely increased extraction yield of dioscin instead of destroying the cell structure.Keywords: D. zingiberensis, ultrasonic waves, extraction, diosgenin

Trajectory Similarity Measurement: An Efficiency Perspective

Trajectories that capture object movement have numerous applications, in which similarity computation between trajectories often plays a key role. Traditionally, the similarity between two trajectories is quantified by means of heuristic measures, e.g., Hausdorff or ERP, that operate directly on the trajectories. In contrast, recent studies exploit deep learning to map trajectories to d-dimensional vectors, called embeddings. Then, some distance measure, e.g., Manhattan or Euclidean, is applied to the embeddings to quantify trajectory similarity. The resulting similarities are inaccurate: they only approximate the similarities obtained using the heuristic measures. As distance computation on embeddings is efficient, focus has been on achieving embeddings yielding high accuracy. Adopting an efficiency perspective, we analyze the time complexities of both the heuristic and the learning-based approaches, finding that the time complexities of the former approaches are not necessarily higher. Through extensive experiments on open datasets, we find that, on both CPUs and GPUs, only a few learning-based approaches can deliver the promised higher efficiency, when the embeddings can be pre-computed, while heuristic approaches are more efficient for one-off computations. Among the learning-based approaches, the self-attention-based ones are the fastest to learn embeddings that also yield the highest accuracy for similarity queries. These results have implications for the use of trajectory similarity approaches given different application requirements