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- Title
Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS.
- Authors
Yan, Donghui; Zhou, Muqing; Adduri, Abhinav; Zhuang, Yihao; Guler, Mustafa; Liu, Sitong; Shin, Hyonyoung; Kovach, Torin; Oh, Gloria; Liu, Xiao; Deng, Yuting; Wang, Xiaofeng; Cao, Liu; Sherman, David H.; Schultz, Pamela J.; Kersten, Roland D.; Clement, Jason A.; Tripathi, Ashootosh; Behsaz, Bahar; Mohimani, Hosein
- Abstract
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. Type 1 polyketides are a major class of natural products with diverse bioactivities but are mostly identified via bioactivity-guided purification which is limited to relatively abundant compounds. Here, the authors present Seq2PKS, a machine learning algorithm that predicts the chemical structures derived from Type 1 polyketide synthases and use it to discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamideactiphenol.
- Subjects
POLYKETIDES; MACHINE learning; MASS spectrometry; POLYKETIDE synthases; MICROBIAL genomes; GENE clusters
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-49587-1