The in vitro synthesis of Coenzyme A (CoA)-thioester intermediates opens new avenues to transform simple molecules into more complex and multifunctional ones by assembling cell-free biosynthetic cascades. In this review, we have systematically cataloged known CoA-dependent enzyme reactions that have been successfully implemented in vitro. To faciliate their identification, we provide their UniProt ID when available. Based on this catalog, we have organized enzymes into three modules: activation, modification, and removal. i) The activation module includes enzymes capable of fusing CoA with organic molecules. ii) The modification module includes enzymes capable of catalyzing chemical modifications in the structure of acyl-CoA intermediates. And iii) the removal module includes enzymes able to remove the CoA and release an organic molecule different from the one activated in the upstream. Based on these reactions, we constructed a reaction network that summarizes the most relevant CoA-dependent biosynthetic pathways reported until today. From the information available in the articles, we have plotted the total turnover number of CoA as a function of the product titer, observing a positive correlation between both parameters. Therefore, the success of a CoA-dependent in vitro pathway depends on its ability to regenerate CoA, but also to regenerate other cofactors such as NAD(P)H and ATP.
Traditional microneedle (MN) technology offers unique advantages in treating wound infections; however, its single-function design lacks the capability for real-time monitoring of wound conditions, often resulting in uncontrolled drug release. Herein, an anti-infective and intelligent MN patch (SP-CSMN) integrating three functional modules is developed, including temperature monitoring, Bluetooth wireless communication, and responsive drug release. The patch employed chitosan (CS) as a porous substrate, filled with temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) to encapsulate and release the antibiotic rifampicin.
Background: Pest infestation poses a major challenge in the field of global plant protection, seriously threatening crop safety. To enhance crop protection and optimize control strategies, this study is dedicated to the precise identification of various pests that harm crops, thereby ensuring the efficient use of agricultural pesticides and achieving optimal plant protection.
Results: Currently, pest identification technologies lack accuracy, especially in recognizing pests across different growth stages.
Key Laboratory of Herbage Improvement and Grassland Agroecosystems, Lanzhou University, Lanzhou, 730020, China; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou, 730020, China; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China; Engineering Research Center of Grassland Industry, Ministry of Education, Gansu Tech Innovation Centre of Western China Grassland Industry, China. Electronic address:
Arbuscular mycorrhizal (AM) fungi are widely existing soil microorganisms that form symbiotic relationships with most terrestrial plants. They are important for enhancing adversity resistance, including resistance to disease and water stresses. Nevertheless, it is not clear whether the benefits can be maintained in regulating the occurrence of plant diseases under drought, flooding stress and during water restoration.
Purpose: Information and communication technologies are crucial for social and professional integration, but access to technology can be difficult for people with physical impairments. Text entry can be slow and tiring. We developed a free and open-source module called for use with AAC (augmentative/alternative communication) software in French language.
Chemoinformatics Group - NEQUIM, Departamento de Quimica, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil.
Traditional testing methods in pharmaceutical development can be time-consuming and costly, but in silico evaluation tools can offer a solution. Our in-house Active-IT system, a Ligand-Based Virtual Screening (LBVS) tool, was developed to predict the biological and pharmacological activities of small organic molecules. It includes four independent modules for generating molecular descriptors (3D-Pharma), machine learning modeling (ExCVBA), a database of bioactivity models, and a prediction module.
