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对微生物基因组中编码的独特聚酮化合物和非核糖体肽结构基序的综合分析。

Comprehensive analysis of distinctive polyketide and nonribosomal peptide structural motifs encoded in microbial genomes.

作者信息

Minowa Yohsuke, Araki Michihiro, Kanehisa Minoru

机构信息

Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto 611-0011, Japan.

出版信息

J Mol Biol. 2007 May 18;368(5):1500-17. doi: 10.1016/j.jmb.2007.02.099. Epub 2007 Mar 14.

Abstract

We developed a highly accurate method to predict polyketide (PK) and nonribosomal peptide (NRP) structures encoded in microbial genomes. PKs/NRPs are polymers of carbonyl/peptidyl chains synthesized by polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS). We analyzed domain sequences corresponding to specific substrates and physical interactions between PKSs/NRPSs in order to predict which substrates (carbonyl/peptidyl units) are selected and assembled into highly ordered chemical structures. The predicted PKs/NRPs were represented as the sequences of carbonyl/peptidyl units to extract the structural motifs efficiently. We applied our method to 4529 PKSs/NRPSs and found 619 PKs/NRPs. We also collected 1449 PKs/NRPs whose chemical structures have been determined experimentally. The structural sequences were compared using the Smith-Waterman algorithm, and clustered into 271 clusters. From the compound clusters, we extracted 33 structural motifs that are significantly related with their bioactivities. We used the structural motifs to infer functions of 13 novel PKs/NRPs clusters produced by Pseudomonas spp. and Burkholderia spp. and found a putative virulence factor. The integrative analysis of genomic and chemical information given here will provide a strategy to predict the chemical structures, the biosynthetic pathways, and the biological activities of PKs/NRPs, which is useful for the rational design of novel PKs/NRPs.

摘要

我们开发了一种高度精确的方法来预测微生物基因组中编码的聚酮化合物(PK)和非核糖体肽(NRP)结构。PKs/NRPs是由聚酮合酶(PKS)和非核糖体肽合成酶(NRPS)合成的羰基/肽基链聚合物。我们分析了与特定底物相对应的结构域序列以及PKSs/NRPSs之间的物理相互作用,以预测哪些底物(羰基/肽基单元)被选择并组装成高度有序的化学结构。预测的PKs/NRPs被表示为羰基/肽基单元的序列,以便有效地提取结构基序。我们将我们的方法应用于4529个PKSs/NRPSs,发现了619个PKs/NRPs。我们还收集了1449个化学结构已通过实验确定的PKs/NRPs。使用Smith-Waterman算法比较结构序列,并将其聚类为271个簇。从化合物簇中,我们提取了33个与其生物活性显著相关的结构基序。我们使用这些结构基序来推断由假单胞菌属和伯克霍尔德菌属产生的13个新型PKs/NRPs簇的功能,并发现了一种假定的毒力因子。此处给出的基因组和化学信息的综合分析将提供一种预测PKs/NRPs的化学结构、生物合成途径和生物活性的策略,这对于新型PKs/NRPs的合理设计是有用的。

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