A deep learning model for type II polyketide natural product prediction without sequence alignment-Reference-Cited by-同舟云学术

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

Published:2023 Issue:5 Volume:2 Page:1484-1493
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Huang Jiaquan¹^ORCID,Gao Qiandi¹^ORCID,Tang Ying²,Wu Yaxin¹^ORCID,Zhang Heqian¹^ORCID,Qin Zhiwei¹^ORCID

Affiliation:

1. Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong, 519087, China

2. International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong, 519087, China

Abstract

Utilizing a large protein language model, we have formulated a deep learning framework designed for predicting type II polyketide natural products.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Beijing Normal University

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00107E

Reference53 articles.

1. The Structural Enzymology of Iterative Aromatic Polyketide Synthases: A Critical Comparison with Fatty Acid Synthases

2. Type II polyketide synthases: gaining a deeper insight into enzymatic teamwork

3. The Biosynthetic Logic of Polyketide Diversity

4. Structural snapshots of the minimal PKS system responsible for octaketide biosynthesis

5. Evolution of chemical diversity by coordinated gene swaps in type II polyketide gene clusters

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