Ric-8 is a highly conserved cytosolic protein (MW 63 KDa) initially identified in C. elegans as an essential factor in neurotransmitter release and asymmetric cell division. Two different isoforms have been described in mammals, Ric-8A and Ric-8B; each possess guanine nucleotide exchange activity (GEF) on heterotrimeric G-proteins, but with different Galpha subunits specificities. To gain insight on the mechanisms involved in Ric-8 cellular functions it is essential to obtain some information about its structure. Therefore, the aim of this work was to create a structural model for Ric-8. In this case, it was not possible to construct a model based on comparison with a template structure because Ric-8 does not present sequence similarity with any other protein. Consequently, different bioinformatics approaches that include protein folding and structure prediction were used. The Ric-8 structural model is composed of 10 armadillo folding motifs, organized in a right-twisted alpha-alpha super helix. In order to validate the structural model, a His-tag fusion construct of Ric-8 was expressed in E. coli, purified by affinity and anion exchange chromatography and subjected to circular dichroism analysis (CD) and thermostability studies. Ric-8 is approximately 80% alpha helix, with a Tm of 43.1 degrees C, consistent with an armadillo-type structure such as alpha-importin, a protein composed of 10 armadillo repeats. The proposed structural model for Ric-8 is intriguing because armadillo proteins are known to interact with multiple partners and participate in diverse cellular functions. These results open the possibility of finding new protein partners for Ric-8 with new cellular functions.
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http://dx.doi.org/10.1002/pro.124 | DOI Listing |
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Alzheimers Res Ther
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Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Crta M40, km38, Madrid, 28223, Spain.
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J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFEye (Lond)
January 2025
Department of Surgical Sciences, University of Turin, Turin, Italy.
Purpose: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-proliferative diabetic retinopathy (NPDR).
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Sci Rep
January 2025
School of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China.
Drums are the core working mechanism of the coal mining machine for coal mining. The structural design level of the drum is crucial for mining efficiency and safety production. Traditional design methods not only have long design cycles and high costs, but also limited design capabilities.
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