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Abstract A prolific multi‐product sesterterpene synthase CbTPS1 is characterized from the medicinal Brassicaceae plant Capsella bursa‐pastoris. Twenty different sesterterpenes including 16 undescribed compounds, possessing 10 different mono‐/di‐/tri‐/tetra‐/penta‐carbocyclic skeletons, including the unique 15‐membered macrocyclic and 24(15→14)‐abeo‐capbuane scaffolds, are isolated and structurally elucidated from engineered Escherichia coli strains expressing CbTPS1. Site‐directed mutagenesis assisted by molecular dynamics simulations resulted in the variant L354M with up to 13.2‐fold increased sesterterpene production. These structurally diverse products suggest a comprehensive cyclization mechanism for plant sesterterpenes and provide compelling evidence for the initial cyclization of geranylfarnesyl diphosphate via a crucial 15‐membered monocyclic carbocation. The activities of these sesterterpenes against liver fibrosis is inferred from the inhibition of the transforming growth factor‐β/Smad signaling pathway and collagen synthesis. These findings greatly expand the chemical space and biological functions of sesterterpenes and provide new insights into the catalytic mechanism of terpene synthases.

With the rapid development of the Internet,the emergence of massive data makes recommender system become a research hotspot in the field of computer science.Variational autoencoders(VAE) have been successfully applied to the design of collaborative filtering methods and achieved excellent recommendation results. However,there are some defects in the previous VAE-based models,such as the problems of prior constraint and the “posterior collapse”,which essentially reduce their recommendation performance.To address this issue while enabling the latent variable model more suitable for the recommendation task,a novel collaborative filtering recommendation model based on latent vector quantization is proposed in this paper.By encoding the discrete vectors instead of directly sampling from the distribution of latent variables,our method can learn discrete representations that are consistent with the observed data,which greatly improves the capability of latent vector encoding and the learning ability of the model.Extensive evaluations conducted on three benchmark datasets demonstrate the effectiveness of the proposed model.Our model can significantly improve the recommendation performance compared with existing state-of-the-art methods while learning more expressive latent representations.