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

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 spec

SUBMITTER: Yan D 

PROVIDER: S-EPMC11199612 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature