Calmodulin (CaM) is a widely studied Ca(2+)-binding protein that is highly conserved across species and involved in many biological processes, including vesicle release, cell proliferation, and apoptosis. To facilitate biophysical studies of CaM, researchers have tagged and mutated CaM at various sites, enabling its conjugation to fluorophores, microarrays, and other reactive partners. However, previous attempts to add a reactive label to CaM for downstream studies have generally employed nonselective labeling methods or resulted in diminished CaM function. Here we report the first engineered CaM protein that undergoes site-specific and bioorthogonal labeling while retaining wild-type activity levels. By employing a chemoenzymatic labeling approach, we achieved selective and quantitative labeling of the engineered CaM protein with an N-terminal 12-azidododecanoic acid tag; notably, addition of the tag did not interfere with the ability of CaM to bind Ca(2+) or a partner protein. The specificity of our chemoenzymatic labeling approach also allowed for selective conjugation of CaM to reactive partners in bacterial cell lysates, without intermediate purification of the engineered protein. Additionally, we prepared CaM-affinity resins that were highly effective in purifying a representative CaM-binding protein, demonstrating that the engineered CaM remains active even after surface capture. Beyond studies of CaM and CaM-binding proteins, the protein engineering and surface capture methods described here should be translatable to other proteins and other bioconjugation applications.
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http://dx.doi.org/10.1021/acs.bioconjchem.5b00449 | DOI Listing |
Front Robot AI
December 2024
School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China.
To address the problems of the labeling curved surfaces vegetable with long label, such as the label wrinkled and the easy detachment, a cam-elliptical gear combined labeling mechanism with an improved hypocycloid trajectory is proposed. Provide the process of the mechanism, and establish a kinematic model of the mechanism. In order to improve the motion performances of the cam-elliptical gear combined labeling mechanism and avoid labels damage, the NSGA-II algorithm is used to optimize the parameters of the mechanism, resulting in 80 sets of Pareto solutions.
View Article and Find Full Text PDFJ Prosthet Dent
December 2024
Associate Professor and Department Head, Department of Prosthodontics, University of Ferrara, Ferrara, Italy.
The purpose of this article was to present a novel clinical workflow for the fabrication of complete dentures using computer-aided design and computer-aided manufacturing (CAD-CAM) technology. The dental technique consists of 3 clinical steps and 2 laboratory phases that result in the production of 2 CAD-CAM milled complete denture bases with prefabricated teeth. The integration of analog and digital procedures and materials maximizes their benefits in the planning and fabrication of complete dentures, with the goal of improving clinical outcomes.
View Article and Find Full Text PDFNitric Oxide
December 2024
Key Laboratory for Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. Electronic address:
Background: Osteocytes are crucial for detecting mechanical stimuli and translating them into biochemical responses within the bone. The primary cilium, a cellular 'antenna,' plays a vital role in this process. However, there is a lack of direct correlation between cilium length changes and osteocyte mechanosensitivity changes.
View Article and Find Full Text PDFSci Rep
December 2024
State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210009, China.
The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) in the the onset of postoperative delirium (POD) remains an area of inquiry. We aim to determine whether qEEG could assist in the diagnosis of early POD in cardiac surgery patients. We prospectively studied a cohort of cardiac surgery patients undergoing qEEG for evaluation of altered mental status.
View Article and Find Full Text PDFAm J Otolaryngol
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.
Purpose: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Materials And Methods: The experiment was based on 3057 images (normal, glottic cancer, granuloma, Reinke's Edema, vocal cord cyst, leukoplakia, nodules and polyps) from the dataset Laryngoscope8. A classification model based on deep neural networks was developed and tested.
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