Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

揭示编码负责多种植物代谢物生物合成的酶的基因，对于代谢工程以及植物代谢物衍生药物的生产至关重要。随着越来越多植物物种的多组学数据的可获取以及计算方法的发展，许多重要植物化合物的代谢途径能够被预测，这对更传统的遗传学和/或生物化学方法是一种补充。在此，我们总结了利用基因组、转录组、蛋白质组、相互作用组和/或代谢组数据预测植物代谢途径的最新进展，以及将这些数据与机器学习相结合以进一步改进代谢途径预测的实用性。