Plant growth-promoting rhizobacteria (PGPR) such as Bacillus and Pseudomonas have drawn broad attention and interest due to their agricultural benefits. One of the major benefits of PGPR lies at their biocontrol capabilities against various plant pathogens. The biocontrol capability of PGPR is closely related to its capability of producing various kinds of antimicrobial substances. Major antimicrobial secondary metabolites secreted by PGPR include non-ribosomal lipopeptides (NRLPs), polyketides, ribosomal peptides, phenazines, pyrrolnitrins, etc. This review focuses on the major antimicrobial secondary metabolites produced by Bacillus and Pseudomonas including their classifications, structures, mechanisms of action and genetic regulations. We have also discussed their applications in plant biocontrol and provided insights into future development of improved biocontrol strains using synthetic biology approaches.
Boscalid is a pesticide with the advantages of broad spectrum bactericidal activity, high efficiency, low toxicity, and no cross-resistance with other fungicides currently available on the market. Herein, we report the synthesis of 4′-chloro-2-nitrobiphenyl, a key intermediate of Boscalid using a palladium-catalyzed Suzuki-Miyaura cross-coupling employing the 2-aryl-substituted indenyl phosphine ligand. 4′-Chloro-2-nitrobiphenyl was prepared in 94 % yield on a 100 g scale. This method allows for the industrial production of alimide and active substances bearing a biphenyl moiety.
The latest review published in Nature Reviews Drug Discovery by Michael W. Mullowney and co-authors focuses on the use of artificial intelligence techniques, specifically machine learning, in natural product drug discovery. The authors discussed various applications of AI in this field, such as genome and metabolome mining, structural characterization of natural products, and predicting targets and biological activities of these compounds. They also highlighted the challenges associated with creating and managing large datasets for training algorithms, as well as strategies to address these obstacles. Additionally, the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.