基于NRPS大数据分析的硫代多肽类天然产物分子多样性探索及定向基因挖掘

含硫多肽天然产物，如β-内酰胺以及含噻唑环的其他抗生素在医药健康中占有重要地位，其生物合成主要利用半胱氨酸为硫原子来源由非核糖体多肽合成酶（NRPS）催化完成。因此，针对接受半胱氨酸的腺苷酰化功能域(A功能域)开展定向基因挖掘或许可提高发现具有生物活性的硫代药物先导化合物的效率。我们前期自主开发了可以精准预测NRPS中A功能域底物的程序，可以分析以不同氨基酸为底物的NRPS的分布及进化关系。在此基础上，本项目提出基于进化分析方法，利用细菌基因组大数据，针对以半胱氨酸或其衍生结构作为A结构域底物的NRPS进行系统进化分析，以了解半胱氨酸来源硫代多肽类化合物的多样性及化合物间的进化关系，其次对可能产生新颖结构的硫代多肽基因簇进行定向基因挖掘和活性测试，获得活性非核糖体肽类化合物。本项目为硫代多肽类化合物的大数据定向挖掘提供科学依据，为新颖硫代活性先导化合物的发现提供一定的研究思路。

Sulfur-containing polypeptides, such as β-lactams and other antibiotics containing thiazole ring have been playing an important role in human health. Most of them derived sulfur from cysteine, which is catalyzed by non-ribosomal peptide synthetase (NRPS). Therefore, targeted genome mining on adenylation (A) domains that use cysteine as the substrate would probably improve efficiency to find sulfur-containing lead compounds with great bioactivity. In our previous study, we developed a program which could be used to predict substrates of A domains in NRPS and analyze evolutionary relationship of NRPSs with different substrates. Based on these results, this project proposes to use big data of bacterial genome to target on NRPS that identify cysteine or its derivative amino acids as a substrate of A domain to do phylogenetic analysis. By doing this, we try to understand the chemical diversity and evolutionary relationship of cysteine-derived sulfur-containing peptide natural products, and conduct genome mining on potential gene clusters of novel sulfur-containing polypeptides to obtain some non-ribosomal peptides with great bioactivity. It will provide scientific basis for targeted mining on big data of sulfur-containing polypeptides and offer research ideas for discovery of new sulfur-containing lead compounds.