Gamma-carboxylation, catalyzed by gamma-glutamyl carboxylase (GGCX), is a critical post-translational modification essential for the biological activity of vitamin K-dependent proteins (VKDPs). Mutations in GGCX, depending on their specific location, result in vitamin K-dependent coagulation factor deficiency type 1 (VKCFD1), which encompasses a broad spectrum of clinical manifestations ranging from mild to severe, including bleeding disorders, osteoporosis, and vascular calcification. The limited knowledge of GGCX's structure and functional regions hinders our understanding of the consequences of GGCX mutations and the treatment for VKCFD1. This study aimed to identify key functional regions of GGCX and their interactions with VKDPs to better elucidate the molecular mechanisms underlying these diverse clinical symptoms. Using AlphaFold 3 and molecular dynamics simulations, we developed a complex binding model of GGCX, FIX, and reduced vitamin K, which revealed critical regions and residues involved in their interaction. Site-directed mutagenesis and cell-based assays further validated the model, confirming that multisite and regional cooperative binding of FIX to GGCX plays a key role in modulating gamma-carboxylation efficiency. Additionally, novel residues (I296, M303, M401, M402) were identified as essential for GGCX's dual enzymatic activities: carboxylation and vitamin K epoxidation. We further demonstrated that the spatial proximity of these active sites supports that GGCX's carboxylation and vitamin K epoxidation centers are interconnected, ensuring the efficient coupling of these processes. Our GGCX-FIX binding and carboxylation model aligns with known pathogenic GGCX mutations, providing valuable insights into the molecular basis of coagulation disorders caused by GGCX mutants.
Minor enterprises play a vital role in the development of national economy. However, they have many deficiencies in technology innovation. After a brief introduction to RNMS (Regional Networked Manufacturing System), this paper discusses in detail that RNMS is able to promote innovation of minor enterprises in many ways. This paper poins out that RNMS can improve the core competitiveness of minor enterprises and boost the regional economy.
Quality and safety of agricultural products related to the health of consumers,related to the development of national economy and social harmony and stability.However,in recent years,China is seeing a lot of quality and safety issues that caused serious harm.Consumers attached importance to quality and safety of agricultural products,and increasingly concerned about the quality of agricultural products.There is no doubt that the reason for quality and safety problems are complex.But in the end it is because the legal supervision system is imperfect and even missing.In order to improve the quality and safety regulatory system,legislation,law enforcement and other aspects needed to be started from.To improve the quality management system of agricultural products,the key is to strengthen the safety supervision of the whole course as agriculture products producing,transportation,processing and so on.
Although the concentration of plant hormone abscisic acid (ABA) in crops is very low, it can effectively regulate the growth of crops. Traditional ABA detection methods are limited by expensive instruments and cumbersome sample processing. Therefore, a new method for detecting ABA in ultra-low concentrations is urgently needed. In this paper, a new method for fast and accurate determination of ABA content based on Surface Enhanced Raman Spectroscopy (SERS) was proposed. In this method, a solution of silver-coated gold nanoparticles (Au@Ag) was self-assembled into dense monolayer under the action of capillary gradient force at the air/water interface, which could be transferred to PDMS wafers and filter paper as SERS active substrates. R6G was used as a probe molecule to characterize the Sensitivity and accuracy of PDMS SERS active substrates. When the PDMS active substrate was selected to detect ABA, the detection limit 1×10−11 M was obtained, indicating that PDMS supported core-shell precious metal monolayer substrate can be used as a effective active substrate for detecting ABA. This method is also expected to be applied to the detection of other plant hormones such as corn and soybean.
Phosphatidylinositol 4,5-bisphosphate (PIP2) is an important molecule located at the inner leaflet of cell membrane, where it serves as anchoring sites for a cohort of membrane-associated molecules and as a broad-reaching signaling intermediate. The lipid raft is thought as the major platform recruiting proteins for signal transduction and also known to mediate PIP2 accumulation across the membrane. While the significance of this cross-membrane coupling is increasingly appreciated, it remains unclear whether and how PIP2 senses the dynamic change of the ordered lipid domains over the packed hydrophobic core of the bilayer. Herein, by means of molecular dynamic simulation, we reveal that inner PIP2 molecules can sense the outer lipid domain via inter-leaflet coupling, and the coupling manner is dictated by the acyl chain length of sphingomyelin (SM) partitioned to the lipid domain. Shorter SM promotes membrane domain registration, whereby PIP2 accumulates beneath the domain across the membrane. In contrast, the anti-registration is thermodynamically preferred if the lipid domain has longer SM due to the hydrophobic mismatch between the corresponding acyl chains in SM and PIP2. In this case, PIP2 is expelled by the domain with a higher diffusivity. These results provide molecular insights into the regulatory mechanism of correlation between the outer lipid domain and inner PIP2, both of which are critical components for cell signal transduction.
Biomedical event extraction is an important branch of biomedical information extraction. Event detection is the most important subtask in event extraction, which has been widely concerned. Most of the existing research on event detection is based on traditional machine learning or neural network. However, they ignored the semantic information of the word itself and its event type and the insufficient features of the out-of-vocabulary neologism representation. In this paper, trigger extraction is treated as a sequence labeling problem. We propose a biomedical event detection model based on knowledge injection and model dual channel fine-tuning, which introduces an external biomedical knowledge base, UMLS. This architecture improves our model's ability to capture semantic information about the word itself and its event types, as well as information about out-of-vocabulary neologisms. The experimental results show that the proposed model improves the performance of biomedical event detection, and the F1 value on the MLEE dataset is 83.59%, which outperforms the recent state-of-the-art methods. Moreover, testing our model on GE13, the experimental results are also significantly improved.
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