Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

Recommended citation: Donghui Yan, Muqing Zhou, Abhinav Adduri, Yihao Zhuang, Mustafa Guler, Sitong Liu, Hyonyoung Shin, Torin Kovach, Gloria Oh, Xiao Liu, Yuting Deng, Xiaofeng Wang, Liu Cao, David H. Sherman, Pamela J. Schultz, Roland D. Kersten, Jason A. Clement, Ashootosh Tripathi, Bahar Behsaz, and Hosein Mohimani (2024). “Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS”. Nature Communications. https://doi.org/10.1038/s41467-024-49587-1