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 produc...
Saved in:
Published in | Nature communications Vol. 15; no. 1; pp. 5356 - 15 |
---|---|
Main Authors | , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
25.06.2024
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
ISSN | 2041-1723 2041-1723 |
DOI | 10.1038/s41467-024-49587-1 |
Cover
Summary: | 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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 None USDOE Office of Science (SC) SC0021340 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-024-49587-1 |