Genome mining explained

Genome mining describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions.^[1] It depends on computational technology and bioinformatics tools. The mining process relies on a huge amount of data (represented by DNA sequences and annotations) accessible in genomic databases. By applying data mining algorithms, the data can be used to generate new knowledge in several areas of medicinal chemistry,^{[2] [3]} such as discovering novel natural products.^[4]

History

In the mid- to late 1980s, researchers have increasingly focused on genetic studies with the advancing sequencing technologies.^[5] The GenBank database was established in 1982 for the collection, management, storage, and distribution of DNA sequence data due to the increasing availability of DNA sequences. With the increasing number of genetic data, biotechnological companies have been able to use human DNA sequence to develop protein and antibody drugs through genome mining since 1992.^[6] In the late 1990s, many companies, such as Amgen, Immunec, Genentech were able to develop drugs that progressed to the clinical stage by adopting genome mining.^[7] Since the Human Genome Project was completed in the early 2000, researchers have been sequencing the genomes of many microorganisms.^[8] Subsequently, many of these genomes have been carefully studied to identify new genes and biosynthetic pathways.^[9]

Algorithms

As large quantities of genomic sequence data began to accumulate in public databases, genetic algorithms became important to decipher the enormous collection of genomic data. They are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.^[10] The followings are commonly used genetic algorithms:

• AntiSMASH (Antibiotics and Secondary Metabolite Analysis Shell) addresses secondary metabolite genome pipelines.^[11]
• PRISM (Prediction Informatics for Secondary Metabolites)^[12] is a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides.^[13]
• SIM (Statistically based sequence similarity) method, such as FASTA or PSI-BLAST, infer orthologous homology.^[14]
• BLAST (Basic local alignment search tool) is an approach for rapid sequence comparison.^[15]

Applications

Genome mining applies on the discovery of natural product by facilitating the characterization of novel molecules and biosynthetic pathways.^[16]

Natural product discovery

The production of natural products is regulated by the biosynthetic gene clusters (BGCs) encoded in the microorganism.^[17] By adopting genome mining, the BGCs that produce the target natural product can be predicted.^[18] Some important enzymes responsible for the formation of natural products are polyketide synthases (PKS), non-ribosomal peptide synthases (NRPS), ribosomally and post-translationally modified peptides (RiPPs), and terpenoids, and many more.^[19] Mining for enzymes, researchers can figure out the classes that BGCs encode and compare target gene clusters to known gene clusters.^[20] To verify the relation between the BGCs and natural products, the target BGCs can be expressed by suitable host through the use of molecular cloning.^[21]

Databases and tools

Genetic data has been accumulated in databases. Researchers are able to utilize algorithms to decipher the data accessible from databases for the discovery of new processes, targets, and products.^[10] The following are databases and tools:

• GenBank database provides genomic datasets for analysis.^[22]
• UCSC Genome Browser
• AntiSMASH-DB^{[23] [24]} allows comparing the sequences of newly sequenced BGCs against those of previously predicted and experimentally characterized ones.^[25]
• BIG-FAM ^[26] is a biosynthetic gene cluster family database.^[27]
• DoBISCUIT^[28] is a database of secondary metabolite biosynthetic gene clusters.^[29]
• MIBiG (Minimum Information about a Biosynthetic Gene cluster specification)^[30] provides a standard for annotations and metadata on biosynthetic gene clusters and their molecular products.^[31]
• Interactive tree of life (iTOL)^[32] is a web-based tool for the display, manipulation and annotation of phylogenetic trees.^[33]

Notes and References

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3. Lee N, Hwang S, Kim J, Cho S, Palsson B, Cho BK . Mini review: Genome mining approaches for the identification of secondary metabolite biosynthetic gene clusters in Streptomyces . Computational and Structural Biotechnology Journal . 18 . 1548–1556 . 2020-01-01 . 32637051 . 7327026 . 10.1016/j.csbj.2020.06.024 .
4. Challis GL . Genome mining for novel natural product discovery . Journal of Medicinal Chemistry . 51 . 9 . 2618–2628 . May 2008 . 18393407 . 10.1021/jm700948z .
5. Bains W, Smith GC . A novel method for nucleic acid sequence determination . Journal of Theoretical Biology . 135 . 3 . 303–307 . December 1988 . 3256722 . 10.1016/S0022-5193(88)80246-7 . 1988JThBi.135..303B .
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12. Web site: PRISM . Adapsyn Bioscience .
13. Skinnider MA, Johnston CW, Gunabalasingam M, Merwin NJ, Kieliszek AM, MacLellan RJ, Li H, Ranieri MR, Webster AL, Cao MP, Pfeifle A, Spencer N, To QH, Wallace DP, Dejong CA, Magarvey NA . 6 . Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences . Nature Communications . 11 . 1 . 6058 . November 2020 . 33247171 . 7699628 . 10.1038/s41467-020-19986-1 . 2020NatCo..11.6058S .
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15. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ . Basic local alignment search tool . Journal of Molecular Biology . 215 . 3 . 403–410 . October 1990 . 2231712 . 10.1016/S0022-2836(05)80360-2 .
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17. Rutledge PJ, Challis GL . Discovery of microbial natural products by activation of silent biosynthetic gene clusters . Nature Reviews. Microbiology . 13 . 8 . 509–523 . August 2015 . 26119570 . 10.1038/nrmicro3496 . 6474118 .
18. Belknap KC, Park CJ, Barth BM, Andam CP . Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria . Scientific Reports . 10 . 1 . 2003 . February 2020 . 32029878 . 7005152 . 10.1038/s41598-020-58904-9 . 2020NatSR..10.2003B .
19. Hoffmeister D, Keller NP . Natural products of filamentous fungi: enzymes, genes, and their regulation . Natural Product Reports . 24 . 2 . 393–416 . April 2007 . 17390002 . 10.1039/B603084J .
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21. Gomez-Escribano JP, Bibb MJ . Heterologous expression of natural product biosynthetic gene clusters in Streptomyces coelicolor: from genome mining to manipulation of biosynthetic pathways . Journal of Industrial Microbiology & Biotechnology . 41 . 2 . 425–431 . February 2014 . 24096958 . 10.1007/s10295-013-1348-5 . 15215660 .
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23. Web site: AntiSMASH-DB .
24. Web site: IMG-ABC .
25. Palaniappan K, Chen IA, Chu K, Ratner A, Seshadri R, Kyrpides NC, Ivanova NN, Mouncey NJ . 6 . IMG-ABC v.5.0: an update to the IMG/Atlas of Biosynthetic Gene Clusters Knowledgebase . Nucleic Acids Research . 48 . D1 . D422–D430 . January 2020 . 31665416 . 7145673 . 10.1093/nar/gkz932 .
26. Web site: BIG-FAM .
27. Kautsar SA, Blin K, Shaw S, Weber T, Medema MH . BiG-FAM: the biosynthetic gene cluster families database . Nucleic Acids Research . 49 . D1 . D490–D497 . January 2021 . 33010170 . 7778980 . 10.1093/nar/gkaa812 .
28. Web site: DoBISCUIT.
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30. Web site: MIBiG .
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32. Web site: iTOL .
33. Letunic I, Bork P . Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees . Nucleic Acids Research . 44 . W1 . W242–W245 . July 2016 . 27095192 . 4987883 . 10.1093/nar/gkw290 .