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

Donghui Yan, Muqing Zhou, Abhinav Adduri, Yihao Zhuang, Mustafa Guler, Sitong Liu, Hyonyoung Shin, Torin Kovach, Gloria Oh, Xiao Liu, Yuting Deng, Xiaofeng Wang, Liu Cao, David H. Sherman, Pamela J. Schultz, Roland D. Kersten, Jason A. Clement, Ashootosh Tripathi (), Bahar Behsaz () and Hosein Mohimani ()
Additional contact information
Donghui Yan: Carnegie Mellon University
Muqing Zhou: Carnegie Mellon University
Abhinav Adduri: Carnegie Mellon University
Yihao Zhuang: University of Michigan
Mustafa Guler: Carnegie Mellon University
Sitong Liu: Carnegie Mellon University
Hyonyoung Shin: Carnegie Mellon University
Torin Kovach: Carnegie Mellon University
Gloria Oh: Carnegie Mellon University
Xiao Liu: Carnegie Mellon University
Yuting Deng: Carnegie Mellon University
Xiaofeng Wang: University of Michigan
Liu Cao: Carnegie Mellon University
David H. Sherman: University of Michigan
Pamela J. Schultz: University of Michigan
Roland D. Kersten: University of Michigan
Jason A. Clement: Baruch S. Blumberg Institute
Ashootosh Tripathi: University of Michigan
Bahar Behsaz: Carnegie Mellon University
Hosein Mohimani: Carnegie Mellon University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49587-1

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