TY - JOUR
T1 - Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides
AU - Jian, Bo Siyuan
AU - Chiou, Shao Lun
AU - Hsu, Chun Chia
AU - Ho, Josh
AU - Wu, Yu Wei
AU - Chu, John
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology (MOST), Taiwan, College Student Research Scholarship MOST 110-2813-C-002-030-M (SLC) and Research Project Grants MOST 109-2113-M-002-005-MY3 (JC), MOST 111-2113-M-002-019-MY2 (JC), MOST 110-2221-E-038-019-MY3 (YWW), and MOST 111-2221-E-038-023-MY3 (YWW).
Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.
PY - 2023/2
Y1 - 2023/2
N2 - The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.
AB - The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.
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U2 - 10.1021/acschembio.2c00761
DO - 10.1021/acschembio.2c00761
M3 - Article
C2 - 36820820
AN - SCOPUS:85149037977
SN - 1554-8929
VL - 18
SP - 476
EP - 483
JO - ACS Chemical Biology
JF - ACS Chemical Biology
IS - 3
ER -