关键词: 进化：天然产物, 进化, 基因挖掘, 生物合成改造, 生物信息学

Abstract:

Nature has invented a myriad of natural products through billions of years of evolution. Natural products own unique structural features selected by evolutionary pressure and serve as a treasure trove for drug discovery. The rapid growth of microbial genomic data now provides new opportunities for evolutionary and big data analysis of biosynthetic gene clusters, which not only gives us a clearer picture about the global landscape of natural products, but also enables us to reveal the evolutionary trajectory of natural products. Such holistic understanding of natural products can facilitate the phylogeny-guided genome mining, allow better functional prediction of biosynthetic enzymes, and even open the door to biosynthetic redesign to create non-natural molecules by evolution-guided engineering. The core essence of evolutionary and large-scale bioinformatics lies in that it visualizes the entire sequence space and their distribution of a particular analyte family. Therefore, big data-driven bioinformatics has the potential to answer some challenging questions such as "How many natural products remain to be discovered?", and "How long can natural products discovery be sustainable?" This review summarizes recent advances in the application of evolution and big data-guided bioinformatics for natural products research from several perspectives including: 1) natural product discovery; 2) functional and structural prediction of biosynthetic enzymes and their products; and 3) bioengineering, with the emphasis on the assembly line enzymes such as polyketide synthases and non-ribosomal peptide synthetases. Due to the modular domain architecture they have, the assembly line enzymes have been the main targets for genome mining. The phylogenetic analysis of their domains has shown to be a powerful and effective way to predict their enzymatic function and substrate specificity. Recently, the evolutionary mechanism of the assembly line enzymes has been investigated, and several evolution-guided engineering strategies were shown to have much higher efficiency for the assembly line reprogramming, providing a potential breakthrough for the bioproduction of complex polyketides and peptides. Non-modular enzymes are also discussed with selected representative examples. Finally, we present current challenges and future prospects of big data-driven natural products research.

Key words: natural products, evolution, genome mining, biosynthetic engineering, bioinformatics