基因组挖掘在天然产物发现中的应用和前景

doi:10.12211/2096-8280.2021-012

合成生物学 ›› 2021, Vol. 2 ›› Issue (5): 697-715.DOI: 10.12211/2096-8280.2021-012

基因组挖掘在天然产物发现中的应用和前景

杨谦¹, 程伯涛¹, 汤志军¹, 刘文¹^,²

^1.中国科学院上海有机化学研究所，生命有机化学国家重点实验室，上海 200032
^2.中国科学院上海有机化学研究所，湖州生物制造中心，浙江湖州 313000

收稿日期:2021-01-27 修回日期:2021-04-05 出版日期:2021-11-19 发布日期:2021-11-19
通讯作者: 刘文
作者简介:杨谦（1994—）女，博士，博士后。研究方向为天然产物化学及以基因组扫描为手段的新型天然产物发现。E-mail：yangqian117@sioc.ac.cn
刘文（1971—），男，研究员，博士生导师。研究方向为复杂天然产物的生物合成（遗传学、生物化学和化学），以产量提高和结构多样性为目的组合生物合成，以基因组扫描为手段的新型天然产物发现。E-mail：wliu@mail.sioc.ac.cn
基金资助:
国家重点研发计划(2019YFA0905400);中国科学院B类先导科技专项(XDB20020200);王宽诚率先人才计划

Applications and prospects of genome mining in the discovery of natural products

Qian YANG¹, Botao CHENG¹, Zhijun TANG¹, Wen LIU¹^,²

^1.State Key Laboratory of Bioorganic and Natural Products Chemistry，Shanghai Institute of Organic Chemistry，Chinese Academy of Sciences，Shanghai 200032，China
^2.Huzhou Center of Bio-Synthetic Innovation，Shanghai Institute of Organic Chemistry，Chinese Academy of Sciences，Huzhou 313000，Zhejiang，China

Received:2021-01-27 Revised:2021-04-05 Online:2021-11-19 Published:2021-11-19
Contact: Wen LIU

摘要/Abstract

摘要：

天然产物一直以来都是药物先导化合物的重要来源。在药物发现领域，基因组数据常用来识别潜在的药物靶点或寻找先前被忽视的天然产物的生物合成基因簇。尽管基因组测序发现了微生物和植物中存在大量未开发的化学多样性，然而，仅仅利用传统的分离分析方法获取新的天然产物已经无法满足药物发展的需求。随着基因组时代的到来，数字化的基因组挖掘已经成为天然产物发现的重要组成部分。伴随着高通量测序方法的发展和DNA数据的丰富，各种基因组挖掘方法和工具被开发出来，以指导发现和表征这些天然产物。本文综述了近年来基因组挖掘的网络工具、数据库和方法，着重介绍次级代谢产物生物合成基因簇的挖掘手段，从经典的基因组挖掘到基于抗性基因挖掘、基于系统进化发育的挖掘，并对基因组挖掘在天然产物发现中的地位和前景进行了展望。

关键词: 基因组挖掘, 天然产物, 网络工具, 数据库

Abstract:

Natural products have been an abundant source of leader compounds for new drugs, but traditional isolation and analysis technologies to obtain novel natural products cannot satisfy the requirement for drug discovery. Genomic data have been utilized for identifying potential drug targets, or exploring biosynthesis pathways for natural products that were neglected before. Genome sequencing has unveiled a plethora of undeveloped chemical diversity in microorganisms and plants. From genome sequences, a large amount of information is available, from functional enzymes to conserved patterns/signatures, even potential structures and features that can be interpreted to hunt for new biocatalysts. With the advent of the genomic era, the computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Meanwhile, the development of high-throughput sequencing and the establishment of DNA database, genome mining methods and tools have contributed to the discovery and characterization of these natural products. In spite of the diversity of natural products, the biosynthetic rules and thus the biosynthetic machineries for many of these compounds are often remarkably conserved, which is highlighted in the high amino acid sequence similarity of the core biosynthetic enzymes, such as polyketides synthases (PKS), non-ribosomally peptides synthetases (NRPS), and many others. Besides, most of natural products are considered to be produced by the host to kill or limit the growth of competitors through the inhibition or inactivation of essential housekeeping enzymes. Therefore, accumulating knowledge on the self-resistance mechanisms, for instance, mining for SRE (self-resistance enzyme), have promoted research on natural products. Moreover, a phylogeny-guided mining approach provides a method to quickly screen a large number of microbial genomes or metagenomes to detect new biosynthetic gene clusters of interest, and many web tools and databases have been developed and utilized by researchers to mine for key enzymes. This paper reviews recent advances in the genome mining tools, databases and approaches, with a focus on the ways of mining biosynthetic gene clusters (BGCs) of natural products, from classical genome mining to resistance-based and phylogeny-guided mining, and also include a short overview on status and perspective in the discovery of novel natural products.

Key words: genome mining, natural products, web tools, database

中图分类号:

杨谦, 程伯涛, 汤志军, 刘文. 基因组挖掘在天然产物发现中的应用和前景[J]. 合成生物学, 2021, 2(5): 697-715.

Qian YANG, Botao CHENG, Zhijun TANG, Wen LIU. Applications and prospects of genome mining in the discovery of natural products[J]. Synthetic Biology Journal, 2021, 2(5): 697-715.

图/表 11

参考文献 112

1	NEWMAN D J, CRAGG G M. Natural products as sources of new drugs from 1981 to 2014[J]. Journal of Natural Products, 2016, 79(3): 629-661.
2	NEWMAN D J, CRAGG G M. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019[J]. Journal of Natural Products, 2020, 83 (3): 770-803.
3	LERNER C G, HAJDUK P J, WAGNER R, et al. From bacterial genomes to novel antibacterial agents: Discovery, characterization, and antibacterial activity of compounds that bind to HI0065 (YjeE) from Haemophilus influenzae[J]. Chemical Biology & Drug Design, 2007, 69 (6): 395-404.
4	CHALLIS G L. Genome mining for novel natural product discovery[J]. Journal of Medicinal Chemistry, 2008, 51(9): 2618-2628.
5	ZALLOT R, OBERG N O, GERLT J A. "Democratized" genomic enzymology web tools for functional assignment[J]. Current Opinion in Chemical Biology, 2018, 47: 77-85.
6	WEBER T. In silico tools for the analysis of antibiotic biosynthetic pathways[J]. International Journal of Medical Microbiology, 2014, 304(3/4): 230-235.
7	MEDEMA M H, FISCHBACH M A. Computational approaches to natural product discovery[J]. Nature Chemical Biology, 2015, 11(9): 639-648.
8	UMEMURA M, KOIKE H, MACHIDA M. Motif-independent de novo detection of secondary metabolite gene clusters-toward identification from filamentous fungi[J]. Frontiers in Microbiology, 2015, 6: 371.
9	WEBER T, KIM H U. The secondary metabolite bioinformatics portal: computational tools to facilitate synthetic biology of secondary metabolite production[J]. Synthetic and Systems Biotechnology, 2016, 1(2): 69-79.
10	BACHMANN B O, LANEN S G, BALTZ R H. Microbial genome mining for accelerated natural products discovery: Is a renaissance in the making [J]? Journal of Industrial Microbiology & Biotechnology, 2014, 41(2): 175-184.
11	BODDY C N. Bioinformatics tools for genome mining of polyketide and non-ribosomal peptides[J]. Journal of Industrial Microbiology & Biotechnology, 2014, 41(2): 443-450.
12	SCHEFFLER R J, COLMER S, TYNAN H, et al. Antimicrobials, drug discovery, and genome mining[J]. Applied Microbiology and Biotechnology, 2013, 97(3): 969-978.
13	YAEGASHI J, OAKLEY B R, WANG C C C. Recent advances in genome mining of secondary metabolite biosynthetic gene clusters and the development of heterologous expression systems in Aspergillus nidulans[J]. Journal of Industrial Microbiology & Biotechnology, 2014, 41(2): 433-442.
14	SANTEN J A VAN, KAUTSAR S A, MEDEMA M H, et al. Microbial natural product databases: moving forward in the multi-omics era[J]. Natural Product Reports, 2021,38(1):264-278.
15	SOROKINA M, STEINBECK C. Review on natural products databases: where to find data in 2020[J]. Journal of Cheminformatics, 2020, 12(1): 20.
16	HARBORNE J B. Dictionary of natural products[EB/OL].2015, .
17	BOLTON EVAN E, WANG Y L, THIESSEN P A, et al. PubChem: integrated platform of small molecules and biological activities[J]. Annual Reports in Computational Chemistry, 2010, 4: 217-241.
18	AFENDI F M, OKADA T, YAMAZAKI M, et al. KNApSAcK family databases: Integrated metabolite-plant species databases for multifaceted plant research[J]. Plant and Cell Physiology, 2012, 53(2): e1.
19	CABOCHE S, PUPIN M,LECLÈRE V, al et, Norine: a database of nonribosomal peptides[J]. Nucleic Acids Research, 2008, 36(1): D326-D331.
20	ZIN P P K, WILLIAMS G J, EKINS S. Cheminformatics analysis and modeling with MacrolactoneDB[J]. Scientific Reports, 2020, 10(1): 6284.
21	ZENG X, ZHANG P, HE W D, et al. NPASS: natural product activity and species source database for natural product research, discovery and tool development[J]. Nucleic Acids Research, 2018, 46(D1): D1217-D1222.
22	KLEMENTZ D, DÖRING K, LUCAS X, et al. StreptomeDB 2.0—an extended resource of natural products produced by Streptomycetes[J]. Nucleic Acids Research, 2016, 44(D1): D509-D514.
23	SANTEN J A VAN, JACOB G, SINGH A L, et al. The natural products atlas: an open access knowledge base for microbial natural products discovery[J]. ACS Central Science, 2019, 5(11): 1824-1833.
24	KAUTSAR S A, BLIN K, SHAW S, et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function[J]. Nucleic Acids Research, 2020, 48(D1): D454-D458.
25	WANG M, CARVER J J, PHELAN V V, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking[J]. Nature Biotechnology, 2016, 34(8): 828-837.
26	BENSON D A, CAVANAUGH M, CLARK K, et al. GenBank[J]. Nucleic Acids Research, 2013, 41(D1): D36-D42.
27	CONWAY K R, BODDY C N. ClusterMine360: a database of microbial PKS/NRPS biosynthesis[J]. Nucleic Acids Research, 2013, 41(D1): D402-D407.
28	ICHIKAWA N, SASAGAWA M, YAMAMOTO M, et al. DoBISCUIT: a database of secondary metabolite biosynthetic gene clusters[J]. Nucleic Acids Research, 2013, 41(D1): D408-D414.
29	CHEN I M A, CHU K, PALANIAPPAN K, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes[J]. Nucleic Acids Research, 2019, 47(D1): D666-D677.
30	BLIN K, PASCAL ANDREU V, DE LOS SANTOS E L C, et al. The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters[J]. Nucleic Acids Research, 2019, 47(D1): D625-D630.
31	DIMINIC J, ZUCKO J, RUZIC I T, et al. Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (R-CSDB)[J]. Journal of Industrial Microbiology & Biotechnology, 2013, 40(6): 653-659.
32	MEDEMA M H, KOTTMANN R, YILMAZ P, et al. Minimum information about a biosynthetic gene cluster[J]. Nature Chemical Biology, 2015, 11(9): 625-631.
33	GRIGORIEV I V, NIKITIN R, HARIDAS S, et al. MycoCosm portal: gearing up for 1000 fungal genomes[J]. Nucleic Acids Research, 2014, 42(D1): D699-D704.
34	BLIN K, MEDEMA M H, KOTTMANN R, et al. The antiSMASH database, a comprehensive database of microbial secondary metabolite biosynthetic gene clusters[J]. Nucleic Acids Research, 2017, 45(D1): D555-D559.
35	O′LEARY N A, WRIGHT M W, BRISTER J R, et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation[J]. Nucleic Acids Research, 2016, 44(D1): D733-D745.
36	The UniProt Consortium. UniProt: The universal protein knowledgebase[J]. Nucleic Acids Research, 2017, 45(D1): D158-D169.
37	RADIVOJAC P, CLARK W T, ORON T R, et al. A large-scale evaluation of computational protein function prediction[J]. Nature Methods, 2013, 10(3): 221-227.
38	EL-GEBALI S, MISTRY J, BATEMAN A, et al. The Pfam protein families database in 2019[J]. Nucleic Acids Research, 2019, 47(D1): D427-D432.
39	BLUM M, CHANG H-Y, CHUGURANSKY S, et al. The InterPro protein families and domains database: 20 years on[J]. Nucleic Acids Research, 2021:49(D1):D344-D354.
40	FINN R D, COGGILL P, EBERHARDT R Y, et al. The Pfam protein families database: towards a more sustainable future[J]. Nucleic Acids Research, 2016, 44(D1): D279-D285.
41	BENTLEY S D, CHATER K F, CERDEÑO-TÁRRAGA A M, et al. Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2)[J]. Nature, 2002, 417(6885): 141-147.
42	CAMACHO C, COULOURIS G, AVAGYAN V, et al. BLAST+: architecture and applications[J]. BMC Bioinformatics, 2009, 10: 421.
43	EDDY S R. Accelerated profile HMM searches[J]. PLoS Computational Biology, 2011, 7(10): e1002195.
44	BUCHFINK B, XIE C, HUSON D H. Fast and sensitive protein alignment using DIAMOND[J]. Nature Methods, 2015, 12(1): 59-60.
45	STARCEVIC A, ZUCKO J, SIMUNKOVIC J, et al. ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures[J]. Nucleic Acids Research, 2008, 36(21): 6882-6892.
46	WEBER T, RAUSCH C, LOPEZ P, et al. CLUSEAN: a computer-based framework for the automated analysis of bacterial secondary metabolite biosynthetic gene clusters[J]. Journal of Biotechnology, 2009, 140(1/2): 13-17.
47	LI M H, UNG P M, ZAJKOWSKI J, et al. Automated genome mining for natural products[J]. BMC Bioinformatics, 2009, 10(1): 185.
48	KHALDI N, SEIFUDDIN F T, TURNER G, et al. SMURF: genomic mapping of fungal secondary metabolite clusters[J]. Fungal Genetics and Biology, 2010, 47(9): 736-741.
49	MEDEMA M H, BLIN K, CIMERMANCIC P, et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences[J]. Nucleic Acids Research, 2011, 39(2): W339-W346.
50	BLIN K, SHAW S, STEINKE K, et al. antiSMASH 5.0: Updates to the secondary metabolite genome mining pipeline[J]. Nucleic Acids Research, 2019, 47(W1): W81-W87.
51	MICHAEL A, FISCHBACH C T W. Antibiotics for emerging pathogens[J]. Science, 2009, 325: 1089-1093.
52	STREIT W R, SCHMITZ R A. Metagenomics-the key to the uncultured microbes[J]. Current Opinion in Microbiology, 2004, 7(5): 492-498.
53	CIMERMANCIC P, MEDEMA M H, CLAESEN J, et al. Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters[J]. Cell, 2014, 158(2): 412-421.
54	CRUZ-MORALES P, KOPP J F, MARTINEZ-GUERRERO C, et al. Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model streptomycetes[J]. Genome Biology and Evolution, 2016, 8(6):1906-1916.
55	TAKEDA I, UMEMURA M, KOIKE H, et al. Motif-Independent prediction of a secondary metabolism gene cluster using comparative genomics: Application to sequenced genomes of Aspergillus and ten other filamentous fungal species[J]. DNA Research, 2014, 21(4): 447-457.
56	SANTOS-ABERTURAS J, CHANDRA G, FRATTARUOLO L, et al. Uncovering the unexplored diversity of thioamidated ribosomal peptides in Actinobacteria using the RiPPER genome mining tool[J]. Nucleic Acids Research, 2019, 47(9): 4624-4637.
57	KLOOSTERMAN A M, SHELTON K E, WEZEL G P VAN, et al. RRE-Finder: A genome-mining tool for class-independent RiPP discovery[J]. mSystems, 2020, 5(5): e00267-00220.
58	MERWIN N J, MOUSA W K, DEJONG C A, et al. DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(1): 371-380.
59	TIETZ J I, SCHWALEN C J, PATEL P S, et al. A new genome-mining tool redefines the lasso peptide biosynthetic landscape[J]. Nature Chemical Biology, 2017, 13(5): 470-478.
60	HASTINGS J, DE MATOS P, DEKKER A, et al. The ChEBI reference database and ontology for biologically relevant chemistry: Enhancements for 2013[J]. Nucleic Acids Research, 2013, 41(D1): D456-D463.
61	BENTO A P, GAULTON A, HERSEY A, et al. The ChEMBL bioactivity database: an update[J]. Nucleic Acids Research, 2014, 42(D1): D1083-D1090.
62	PENCE H E, WILLIAMS A. ChemSpider: an online chemical information resource[J]. Journal of Chemical Education, 2010, 87(11): 1123-1124.
63	KAUTSAR S A, BLIN K, SHAW S, et al. BiG-FAM: the biosynthetic gene cluster families database[J]. Nucleic Acids Research, 2020, 49(D1): D490-D497.
64	WEN L. Progress of biosynthesis of enediyne antitumor antibiotics[J]. World Sci-Tech R & D, 2005, 27: 3.
65	ZAZOPOULOS E, HUANG K, STAFFA A, et al. A genomics-guided approach for discovering and expressing cryptic metabolic pathways[J]. Nature Biotechnology, 2003, 21(2): 187-190.
66	ADHIKARI A, TEIJARO C N, TOWNSEND C A, et al. 1.12-biosynthesis of enediyne natural products[M]//LIU H-W, BEGLEY T P. Comprehensive natural products iii. Oxford: Elsevier, 2020: 365-414.
67	SHEN B, HINDRA, YAN X, et al. Enediynes: exploration of microbial genomics to discover new anticancer drug leads[J]. Bioorganic & Medicinal Chemistry Letters, 2015, 25(1): 9-15.
68	YAN X, GE H, HUANG T, et al. Strain prioritization and genome mining for enediyne natural products[J]. mBio, 2016, 7(6): e02104-02116.
69	ORTEGA M A, HAO Y, ZHANG Q, et al. Structure and mechanism of the tRNA-dependent lantibiotic dehydratase NisB[J]. Nature, 2015, 517(7535): 509-512.
70	HUDSON G A, ZHANG Z G, TIETZ J I, et al. In vitro biosynthesis of the core scaffold of the thiopeptide thiomuracin[J]. Journal of the American Chemical Society, 2015, 137(51): 16012-16015.
71	TING C P, FUNK M A, HALABY S L, et al. Use of a scaffold peptide in the biosynthesis of amino acid-derived natural products[J]. Science, 2019, 365(6450): 280-284.
72	HAMADA T, MATSUNAGA S, FUJIWARA M, et al. Solution structure of polytheonamide B, a highly cytotoxic nonribosomal polypeptide from marine sponge[J]. Journal of the American Chemical Society, 2010, 132(37): 12941-12945.
73	MORINAKA B I, VAGSTAD A L, HELF M J, et al. Radical S-adenosyl methionine epimerases: Regioselective introduction of diverse D-amino acid patterns into peptide natural products[J]. Angewandte Chemie International Edition, 2014, 53(32): 8503-8507.
74	FREEMAN M F, GURGUI C, HELF M J, et al. Metagenome mining reveals polytheonamides as posttranslationally modified ribosomal peptides[J]. Science, 2012, 338(6105): 387-390.
75	BHUSHAN A, EGLI P J, PETERS E E, et al. Genome mining- and synthetic biology-enabled production of hypermodified peptides[J]. Nature Chemistry, 2019, 11(10): 931-939.
76	BRODERICK J B, DUFFUS B R, DUSCHENE K S, et al. Radical S-adenosylmethionine enzymes[J]. Chemical Reviews, 2014, 114(8): 4229-4317.
77	BENJDIA A, GUILLOT A, LEFRANC B, et al. Thioether bond formation by SPASM domain radical SAM enzymes: C_α H-atom abstraction in subtilosin A biosynthesis[J]. Chemical Communications, 2016, 52(37): 6249-6252.
78	HUDSON G A, BURKHART B J, DICAPRIO A J, et al. Bioinformatic mapping of radical S-adenosylmethionine-dependent ribosomally synthesized and post-translationally modified peptides identifies new C_α, C_β, and C_γ-linked thioether-containing peptides[J]. Journal of the American Chemical Society, 2019, 141(20): 8228-8238.
79	SCHRAMMA K R, BUSHIN L B, SEYEDSAYAMDOST M R. Structure and biosynthesis of a macrocyclic peptide containing an unprecedented lysine-to-tryptophan crosslink[J]. Nature Chemistry, 2015, 7(5): 431-437.
80	GARDAN R, BESSET C, GUILLOT A, et al. The Oligopeptide transport system is essential for the development of natural competence in Streptococcus thermophilus strain LMD-9[J]. Journal of Bacteriology, 2009, 191(14): 4647-4655.
81	BUSHIN L B, CLARK K A, PELCZER I, et al. Charting an unexplored streptococcal biosynthetic landscape reveals a unique peptide cyclization motif[J]. Journal of the American Chemical Society, 2018, 140(50): 17674-17684.
82	CARUSO A, MARTINIE R J, BUSHIN L B, et al. Macrocyclization via an arginine-tyrosine crosslink broadens the reaction scope of radical S-adenosylmethionine enzymes[J]. Journal of the American Chemical Society, 2019, 141(42): 16610-16614.
83	CLARK K A, BUSHIN L B, SEYEDSAYAMDOST M R. Aliphatic ether bond formation expands the scope of radical SAM enzymes in natural product biosynthesis[J]. Journal of the American Chemical Society, 2019, 141(27): 10610-10615.
84	CARUSO A, BUSHIN L B, CLARK K A, et al. Radical approach to enzymatic β-thioether bond formation[J]. Journal of the American Chemical Society, 2019, 141(2): 990-997.
85	BUSHIN L B, COVINGTON B C, RUED B E, et al. Discovery and biosynthesis of streptosactin, a sactipeptide with an alternative topology encoded by commensal bacteria in the human microbiome[J]. Journal of the American Chemical Society, 2020, 142(38): 16265-16275.
86	LAUTRU S, DEETH R J, BAILEY L M, et al. Discovery of a new peptide natural product by Streptomyces coelicolor genome mining[J]. Nature Chemical Biology, 2005, 1(5): 265-269.
87	YAN Y, LIU N, TANG Y. Recent developments in self-resistance gene directed natural product discovery[J]. Natural Product Reports, 2020, 37(7): 879-892.
88	GALM U, HAGER M H, LANEN S G VAN, et al. Antitumor antibiotics: bleomycin, enediynes, and mitomycin[J]. Chemical Reviews, 2005, 105(2): 739-758.
89	WEISBLUM B. Erythromycin resistance by ribosome modification[J]. Antimicrobial Agents and Chemotherapy, 1995, 39(3): 577-585.
90	O'NEILL E C, SCHORN M, LARSON C B, et al. Targeted antibiotic discovery through biosynthesis-associated resistance determinants: Target directed genome mining[J]. Critical Reviews in Microbiology, 2019, 45(3): 255-277.
91	ALMABRUK K H, DINH L K, PHILMUS B. Self-resistance of natural product producers: past, present, and future focusing on self-resistant protein variants[J]. ACS Chemical Biology, 2018, 13(6): 1426-1437.
92	MAXWELL A. The interaction between coumarin drugs and DNA gyrase[J]. Molecular Microbiology, 1993, 9(4): 681-686.
93	THIARA A S, CUNDLIFFE E. Interplay of novobiocin-resistant and -sensitive DNA gyrase activities in self-protection of the novobiocin producer, Streptomyces sphaeroides[J]. Gene, 1989, 81(1): 65-72.
94	STEFFENSKY M, MÜHLENWEG A, WANG Z-X, et al. Identification of the novobiocin biosynthetic gene cluster of Streptomyces spheroides NCIB 11891[J]. Antimicrobial Agents and Chemotherapy, 2000, 44(5): 1214-1222.
95	EL-SAYED A K, HOTHERSALL J, COOPER S M, et al. Characterization of the mupirocin biosynthesis gene cluster from Pseudomonas fluorescens NCIMB 10586[J]. Chemistry & Biology, 2003, 10(5): 419-430.
96	FUKUDA D, HAINES A S, SONG Z, et al. A natural plasmid uniquely encodes two biosynthetic pathways creating a potent anti-MRSA antibiotic[J]. PLoS One, 2011, 6(3): e18031.
97	OLANO C, WILKINSON B, SÁNCHEZ C, et al. Biosynthesis of the angiogenesis inhibitor Borrelidin by Streptomyces parvulus Tü4055: Cluster Analysis and Assignment of Functions [J]. Chemistry & Biology, 2004, 11(1): 87-97.
98	STANCU C, SIMA A. Statins: mechanism of action and effects[J]. Journal of Cellular and Molecular Medicine, 2001, 5(4): 378-387.
99	HUTCHINSON C R, KENNEDY J, PARK C, et al. Aspects of the biosynthesis of non-aromatic fungal polyketides by iterative polyketide synthases[J]. Antonie van Leeuwenhoek, 2000, 78(3/4): 287-295.
100	CHAMILOS G, LEWIS R E, KONTOYIANNIS D P. Lovastatin has significant activity against zygomycetes and interacts synergistically with voriconazole[J]. Antimicrobial Agents and Chemotherapy, 2006, 50(1): 96-103.
101	AMORIM FRANCO T M, BLANCHARD J S. Bacterial branched-chain amino acid biosynthesis: structures, mechanisms, and drugability[J]. Biochemistry, 2017, 56(44): 5849-5865.
102	YAN Y, LIU Q, ZANG X, et al. Resistance-gene-directed discovery of a natural-product herbicide with a new mode of action[J]. Nature, 2018, 559(7714): 415-418.
103	TANG M C, ZOU Y, YEE D, et al. Identification of the pyranonigrin A biosynthetic gene cluster by genome mining in Penicillium thymicola IBT 5891[J]. AIChE Journal, 2018, 64(12): 4182-4186.
104	KANG H-S. Phylogeny-guided (meta) genome mining approach for the targeted discovery of new microbial natural products[J]. Journal of Industrial Microbiology & Biotechnology, 2017, 44(2): 285-293.
105	FENG Z, KALLIFIDAS D, BRADY S F. Functional analysis of environmental DNA-derived type II polyketide synthases reveals structurally diverse secondary metabolites[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(31): 12629-12634.
106	HERTWECK C. The biosynthetic logic of polyketide diversity[J]. Angewandte Chemie International Edition, 2009, 48(26): 4688-4716.
107	HILLENMEYER M E, VANDOVA G A, BERLEW E E, et al. Evolution of chemical diversity by coordinated gene swaps in type II polyketide gene clusters[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(45): 13952-13957.
108	KANG H-S, BRADY S F. Mining soil metagenomes to better understand the evolution of natural product structural diversity: Pentangular polyphenols as a case study[J]. Journal of the American Chemical Society, 2014, 136(52): 18111-18119.
109	MINOTTI G, MENNA P, SALVATORELLI E, et al. Anthracyclines: Molecular advances and pharmacologic developments in antitumor activity and cardiotoxicity[J]. Pharmacological Reviews, 2004, 56(2): 185.
110	TACAR O, SRIAMORNSAK P, DASS C R. Doxorubicin: An update on anticancer molecular action, toxicity and novel drug delivery systems[J]. Journal of Pharmacy and Pharmacology, 2013, 65(2): 157-170.
111	KANG H S, BRADY S F. Arimetamycin A: improving clinically relevant families of natural products through sequence-guided screening of soil metagenomes[J]. Angewandte Chemie International Edition, 2013, 52(42): 11063-11067.
112	KANEHISA M, FURUMICHI M, TANABE M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs[J]. Nucleic Acids Research, 2017, 45(D1): D353-D361.

数据库或web工具	网址（URL）	参考文献
天然产物数据库
Dictionary of Natural Products（DNP）	http://dnp.chemnetbase.com	[16]
The Natural Products Atlas	https://www.npatlas.org	[23]
PubMed	https://pubmed.ncbi.nlm.nih.gov/
NPASS	http://bidd2.nus.edu.sg/NPASS	[21]
StreptomeDB	http://132.230.56.4/streptomedb2/	[20]
MarinLit	http://pubs.rsc.org/marinlit/
AntiBase	https://sciencesolutions.wiley.com
KNApSAcK	http://kanaya.naist.jp/KNApSAcK/	[18]
Norine	https://bioinfo.lifl.fr/norine/	[19]
MacrolactoneDB	https://macrolact.collaborationspharma.com/	[20]
ChEBI	http://www.ebi.ac.uk/chebi/	[60]
ChEMBl	https://www.ebi.ac.uk/chembl/	[61]
ChemSpider	http://www.chemspider.com/	[62]
COCONUT	https://doi.org/10.5281/zenod	[15]
生物合成基因簇数据库
ClusterMine360	http://www.clustermine360.ca/	[27]
DoBISCUIT	http://www.bio.nite.go.jp/pks/	[28]
MIBiG	https://mibig.secondarymetabolites.org/	[24]
IMG-ABC	https://img.jgi.doe.gov/cgi-bin/abc/main.cgi	[29]
antiSMASH Database	https://antismash.secondarymetabolites.org/	[30]
ClustScan Database	http://csdb.bioserv.pbf.hr/csdb/	[45]
BiG-FAM	https://bigfam.bioinformatics.nl/	[63]
蛋白家族数据库
UniProtKB	https://www.uniprot.org/	[36]
Pfam	http://pfam.xfam.org/	[40]
InterPro	http://www.ebi.ac.uk/interpro/	[39]
识别生物合成基因簇的网络工具
BLAST	https://blast.ncbi.nlm.nih.gov/Blast.cgi	[42]
HMMer	http://hmmer.org/	[43]
ClustScan	http://bioserv.pbf.hr/cms/	[31]
np.searcher	http://dna.sherman.lsi.umich.edu/	[47]
SMURF	http://jcvi.org/smurf/index.php	[48]
antiSMASH	http://antismash.secondarymetabolites.org	[49-50]
ClusterFinder	https://github.com/petercim/ClusterFinder	[53]
RODEO	http://rodeo.scs.illinois.edu/	[59]

数据库或web工具	网址（URL）	参考文献
天然产物数据库
Dictionary of Natural Products（DNP）	http://dnp.chemnetbase.com	[16]
The Natural Products Atlas	https://www.npatlas.org	[23]
PubMed	https://pubmed.ncbi.nlm.nih.gov/
NPASS	http://bidd2.nus.edu.sg/NPASS	[21]
StreptomeDB	http://132.230.56.4/streptomedb2/	[20]
MarinLit	http://pubs.rsc.org/marinlit/
AntiBase	https://sciencesolutions.wiley.com
KNApSAcK	http://kanaya.naist.jp/KNApSAcK/	[18]
Norine	https://bioinfo.lifl.fr/norine/	[19]
MacrolactoneDB	https://macrolact.collaborationspharma.com/	[20]
ChEBI	http://www.ebi.ac.uk/chebi/	[60]
ChEMBl	https://www.ebi.ac.uk/chembl/	[61]
ChemSpider	http://www.chemspider.com/	[62]
COCONUT	https://doi.org/10.5281/zenod	[15]
生物合成基因簇数据库
ClusterMine360	http://www.clustermine360.ca/	[27]
DoBISCUIT	http://www.bio.nite.go.jp/pks/	[28]
MIBiG	https://mibig.secondarymetabolites.org/	[24]
IMG-ABC	https://img.jgi.doe.gov/cgi-bin/abc/main.cgi	[29]
antiSMASH Database	https://antismash.secondarymetabolites.org/	[30]
ClustScan Database	http://csdb.bioserv.pbf.hr/csdb/	[45]
BiG-FAM	https://bigfam.bioinformatics.nl/	[63]
蛋白家族数据库
UniProtKB	https://www.uniprot.org/	[36]
Pfam	http://pfam.xfam.org/	[40]
InterPro	http://www.ebi.ac.uk/interpro/	[39]
识别生物合成基因簇的网络工具
BLAST	https://blast.ncbi.nlm.nih.gov/Blast.cgi	[42]
HMMer	http://hmmer.org/	[43]
ClustScan	http://bioserv.pbf.hr/cms/	[31]
np.searcher	http://dna.sherman.lsi.umich.edu/	[47]
SMURF	http://jcvi.org/smurf/index.php	[48]
antiSMASH	http://antismash.secondarymetabolites.org	[49-50]
ClusterFinder	https://github.com/petercim/ClusterFinder	[53]
RODEO	http://rodeo.scs.illinois.edu/	[59]

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基因组挖掘在天然产物发现中的应用和前景

Applications and prospects of genome mining in the discovery of natural products

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