Japan Agency for Medical Research and Development (AMED)
JP20am0101071
日本
引用
ジャーナル: Angew Chem Int Ed Engl / 年: 2021 タイトル: Structural and Functional Analyses of the Tridomain-Nonribosomal Peptide Synthetase FmoA3 for 4-Methyloxazoline Ring Formation. 著者: Yohei Katsuyama / Kaoru Sone / Ayaka Harada / Seiji Kawai / Naoki Urano / Naruhiko Adachi / Toshio Moriya / Masato Kawasaki / Kazuo Shin-Ya / Toshiya Senda / Yasuo Ohnishi / 要旨: Nonribosomal peptide synthetases (NRPSs) are attractive targets for bioengineering to generate useful peptides. FmoA3 is a single modular NRPS composed of heterocyclization (Cy), adenylation (A), and ...Nonribosomal peptide synthetases (NRPSs) are attractive targets for bioengineering to generate useful peptides. FmoA3 is a single modular NRPS composed of heterocyclization (Cy), adenylation (A), and peptidyl carrier protein (PCP) domains. It uses α-methyl-l-serine to synthesize a 4-methyloxazoline ring, probably with another Cy domain in the preceding module FmoA2. Here, we determined the head-to-tail homodimeric structures of FmoA3 by X-ray crystallography (apo-form, with adenylyl-imidodiphosphate and α-methyl-l-seryl-AMP) and cryogenic electron microscopy single particle analysis, and performed site-directed mutagenesis experiments. The data revealed that α-methyl-l-serine can be accommodated in the active site because of the extra space around Ala688. The Cy domains of FmoA2 and FmoA3 catalyze peptide bond formation and heterocyclization, respectively. FmoA3's Cy domain seems to lose its donor PCP binding activity. The collective data support a proposed catalytic cycle of FmoA3.
EMPIAR-11059 (タイトル: Structural and functional analyses of the tridomain-nonribosomal peptide synthetase FmoA3 for 4-methyloxazoline ring formation Data size: 1.4 TB Data #1: Structural and functional analyses of the tridomain-nonribosomal peptide synthetase FmoA3 for 4-methyloxazoline ring formation [micrographs - multiframe])
モデルのタイプ: OTHER 詳細: An ab initio model was generated using RELION3's own implementation of Stochastic Gradient Descent (SGD) algorithm and low-pass filtered to 60 A for use as an initial model for 3D classification.