Articles tagged with Substrate Specificity
A five-year study reveals that prolonged nitrogen deposition affects subtropical forest soil microbiomes, with a dynamic temporal shift in microbial community assembly and network stability. Dissolved organic matter quality is crucial in predicting bacterial network complexity.
A new AI-powered tool, EZSpecificity, can predict the best enzyme-substrate combination for various applications. The tool outperformed existing models in accuracy, especially for halogenase enzymes.
Scientists from the University of Bath have identified two new families of chemical compounds that inhibit alpha-methylacyl-CoA racemase (MCR) in Mycobacterium tuberculosis, a key enzyme for TB survival. This breakthrough could lead to new treatments for TB and potentially other diseases like prostate cancer.
A Kobe University team developed a technique to classify thousands of enzymes, allowing for rapid evaluation and identification of highly active and versatile enzymes. The approach enabled the discovery of an enzyme with up to 10 times higher productivity than industry standards.
Researchers at Kobe University have created a new test feed for the fungal molecular machine, allowing them to observe its close-to-natural action. This breakthrough enables the characterization of the enzyme's reaction speed and affinity, crucial parameters for industrial application.
Researchers at Kumamoto University have discovered the key to hMTH1's ability to hydrolyze multiple oxidized dNTPs with high efficiency. The protonation state of specific aspartate residues plays a crucial role in this process, allowing for targeted inhibition of cancer cells.