{Reference Type}: Journal Article
{Title}: Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS.
{Author}: Yan D;Zhou M;Adduri A;Zhuang Y;Guler M;Liu S;Shin H;Kovach T;Oh G;Liu X;Deng Y;Wang X;Cao L;Sherman DH;Schultz PJ;Kersten RD;Clement JA;Tripathi A;Behsaz B;Mohimani H;
{Journal}: Nat Commun
{Volume}: 15
{Issue}: 1
{Year}: 2024 Jun 25
{Factor}: 17.694
{DOI}: 10.1038/s41467-024-49587-1
{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.