Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS.
Nat Commun
; 15(1): 5356, 2024 Jun 25.
Article
in En
| MEDLINE
| ID: mdl-38918378
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.
Full text:
1
Database:
MEDLINE
Main subject:
Mass Spectrometry
/
Multigene Family
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Polyketide Synthases
/
Polyketides
Language:
En
Year:
2024
Type:
Article