Preparation of a protein sample for liquid-state nuclear magnetic resonance (NMR) spectroscopy analysis requires optimization of many parameters. This review describes labeling strategies for obtaining assignments of protein resonances. Particular emphasis is placed on the advantages of cell-free protein production, which enables exclusive labeling of the protein of interest, thereby simplifying downstream processing steps and increasing the availability of different labeling strategies for a target protein. Furthermore, proteins can be synthesized in milligram yields, and the open nature of the cell-free system allows the addition of stabilizers, scrambling inhibitors or hydrophobic solubilization environments directly during the protein synthesis, which is especially beneficial for membrane proteins. Selective amino acid labeling of the protein of interest, the possibility of addressing scrambling issues and avoiding the need for labile amino acid precursors have been key factors in enabling the introduction of new assignment strategies based on different labeling schemes as well as on new pulse sequences. Combinatorial selective labeling methods have been developed to reduce the number of protein samples necessary to achieve a complete backbone assignment. Furthermore, selective labeling helps to decrease spectral overlap and overcome size limitations for solution NMR analysis of larger complexes, oligomers, intrinsically disordered proteins and membrane proteins.
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http://dx.doi.org/10.1016/j.pnmrs.2017.11.004 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan.
Recently, the application of deep neural networks to detect anomalies on medical images has been facing the appearance of noisy labels, including overlapping objects and similar classes. Therefore, this study aims to address this challenge by proposing a unique attention module that can assist deep neural networks in focusing on important object features in noisy medical image conditions. This module integrates global context modeling to create long-range dependencies and local interactions to enable channel attention ability by using 1D convolution that not only performs well with noisy labels but also consumes significantly less resources without any dimensionality reduction.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.
: Developing high-performance artificial intelligence (AI) models for rare diseases is challenging owing to limited data availability. This study aimed to evaluate whether a novel three-class annotation method for preparing training data could enhance AI model performance in detecting osteosarcoma on plain radiographs compared to conventional single-class annotation. : We developed two annotation methods for the same dataset of 468 osteosarcoma X-rays and 378 normal radiographs: a conventional single-class annotation (1C model) and a novel three-class annotation method (3C model) that separately labeled intramedullary, cortical, and extramedullary tumor components.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Britton Chance Laboratory of Redox Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
: Cancer cells rely on metabolic reprogramming that is supported by altered mitochondrial redox status and an increased demand for NAD. Over expression of Nampt, the rate-limiting enzyme of the NAD biosynthesis salvage pathway, is common in breast cancer cells, and more so in triple negative breast cancer (TNBC) cells. Targeting the salvage pathway has been pursued for cancer therapy.
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