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Probing Biomolecular Interactions with Paramagnetic Nuclear Magnetic Resonance Spectroscopy.
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Institute for Drug Discovery, University of Leipzig, Brüderstr. 34, 04103, Leipzig, Germany.
Recent advances in computational methods like AlphaFold have transformed structural biology, enabling accurate modeling of protein complexes and driving applications in drug discovery and protein engineering. However, predicting the structure of systems involving weak, transient, or dynamic interactions, or of complexes with disordered regions, remains challenging. Nuclear Magnetic Resonance (NMR) spectroscopy offers atomic-level insights into biomolecular complexes, even in weakly interacting and dynamic systems.
View Article and Find Full Text PDFBiochemical characterization and molecular docking of a novel alkaline-stable keratinase from Amycolatopsis sp. BJA-103.
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College of Life Science, Northwest A&F University, Yangling 712100, China. Electronic address:
Amycolatopsis sp. BJA-103 was isolated for its exceptional feather-degradation capability, leading to the purification, cloning, and heterologous expression of the keratinase enzyme, KER0199. Sequence analysis places KER0199 within the S8 protease family, revealing <60 % sequence similarity to known proteases.
View Article and Find Full Text PDFThe potential of bacteriocins and bacteriophages to control bacterial disease of crops with a focus on spp.
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School of Life Sciences, University of Warwick, Innovation Campus, Stratford-upon-Avon, UK.
Crop production plays a crucial role in ensuring global food security and maintaining economic stability. The presence of bacterial phytopathogens, particularly species (a key focus of this review), poses significant threats to crops, leading to substantial economic losses. Current control strategies, such as the use of chemicals and antibiotics, face challenges such as environmental impact and the development of antimicrobial resistance.
View Article and Find Full Text PDFMaking sense of missense: challenges and opportunities in variant pathogenicity prediction.
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The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH 43215, USA.
Computational tools for predicting variant pathogenicity are widely used to support clinical variant interpretation. Recently, several models, which do not rely on known variant classifications during training, have been developed. These approaches can potentially overcome biases of current clinical databases, such as misclassifications, and can potentially better generalize to novel, unclassified variants.
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