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Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product scr...

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Autores principales: Skinnider, Michael A., Dejong, Chris A., Rees, Philip N., Johnston, Chad W., Li, Haoxin, Webster, Andrew L. H., Wyatt, Morgan A., Magarvey, Nathan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4787774/
https://www.ncbi.nlm.nih.gov/pubmed/26442528
http://dx.doi.org/10.1093/nar/gkv1012
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author Skinnider, Michael A.
Dejong, Chris A.
Rees, Philip N.
Johnston, Chad W.
Li, Haoxin
Webster, Andrew L. H.
Wyatt, Morgan A.
Magarvey, Nathan A.
author_facet Skinnider, Michael A.
Dejong, Chris A.
Rees, Philip N.
Johnston, Chad W.
Li, Haoxin
Webster, Andrew L. H.
Wyatt, Morgan A.
Magarvey, Nathan A.
author_sort Skinnider, Michael A.
collection PubMed
description Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/.
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spelling pubmed-47877742016-03-14 Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM) Skinnider, Michael A. Dejong, Chris A. Rees, Philip N. Johnston, Chad W. Li, Haoxin Webster, Andrew L. H. Wyatt, Morgan A. Magarvey, Nathan A. Nucleic Acids Res Computational Biology Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/. Oxford University Press 2015-11-16 2015-10-05 /pmc/articles/PMC4787774/ /pubmed/26442528 http://dx.doi.org/10.1093/nar/gkv1012 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Skinnider, Michael A.
Dejong, Chris A.
Rees, Philip N.
Johnston, Chad W.
Li, Haoxin
Webster, Andrew L. H.
Wyatt, Morgan A.
Magarvey, Nathan A.
Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title_full Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title_fullStr Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title_full_unstemmed Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title_short Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)
title_sort genomes to natural products prediction informatics for secondary metabolomes (prism)
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4787774/
https://www.ncbi.nlm.nih.gov/pubmed/26442528
http://dx.doi.org/10.1093/nar/gkv1012
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