Issue 5, 2023

A deep learning model for type II polyketide natural product prediction without sequence alignment

Abstract

Natural products are important sources for drug development, and the accurate prediction of their structures assembled by modular proteins is an area of great interest. In this study, we introduce DeepT2, an end-to-end, cost-effective, and accurate machine learning platform to accelerate the identification of type II polyketides (T2PKs), which represent a significant portion of the natural product world. Our algorithm is based on advanced natural language processing models and utilizes the core biosynthetic enzyme, chain length factor (CLF or KSβ), as computing inputs. The process involves sequence embedding, data labeling, classifier development, and novelty detection, which enable precise classification and prediction directly from KSβ without sequence alignments. Combined with metagenomics and metabolomics, we evaluated the ability of DeepT2 and found this model could easily detect and classify KSβ either as a single sequence or a mixture of bacterial genomes, and subsequently identify the corresponding T2PKs in a labeled categorized class or as novel. Our work highlights deep learning as a promising framework for genome mining and therefore provides a meaningful platform for discovering medically important natural products. The DeepT2 is available at GitHub repository: https://github.com/Qinlab502/deept2.

Graphical abstract: A deep learning model for type II polyketide natural product prediction without sequence alignment

Supplementary files

Article information

Article type
Paper
Submitted
08 Jūn. 2023
Accepted
29 Aug. 2023
First published
30 Aug. 2023
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2023,2, 1484-1493

A deep learning model for type II polyketide natural product prediction without sequence alignment

J. Huang, Q. Gao, Y. Tang, Y. Wu, H. Zhang and Z. Qin, Digital Discovery, 2023, 2, 1484 DOI: 10.1039/D3DD00107E

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