Tandem Prenyltransferases Catalyze Isoprenoid Elongation and Complexity Generation in Biosynthesis of Quinolone Alkaloids

Modification of natural products with prenyl groups and the ensuing oxidative transformations are important for introducing structural complexity and biological activities. Penigequinolones (<b>1</b>) are potent insecticidal alkaloids that contain a highly modified 10-carbon prenyl group. Here we reveal an iterative prenylation mechanism for installing the 10-carbon unit using two aromatic prenyltransferases (PenI and PenG) present in the gene cluster of <b>1</b> from Penicillium thymicola. The initial Friedel–Crafts alkylation is catalyzed by PenI to yield dimethylallyl quinolone <b>6</b>. The five-carbon side chain is then dehydrogenated by a flavin-dependent monooxygenase to give aryl diene <b>9</b>, which serves as the electron-rich substrate for a second alkylation with dimethylallyl diphosphate to yield stryrenyl product <b>10</b>. The completed, oxidized 10-carbon prenyl group then undergoes further structural morphing to yield yaequinolone C (<b>12</b>), the immediate precursor of <b>1</b>. Our studies have therefore uncovered an unprecedented prenyl chain extension mechanism in natural product biosynthesis

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target “undruggable” proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications