Abstract
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/.
Highlights
Natural products represent the basis for the majority of small molecule drugs currently in clinical use, due in part to their diverse and unique chemical scaffolds [1]
PRISM implements a library of profile hidden Markov models to facilitate more accurate monomer prediction than existing methods, which rely on the identification of a substrate specificity-conferring code
PRISM integrates a third family of substrate-activating domains, a large clade of acyl-adenylating enzymes revealed by phylogenetic analysis, including domains responsible for activating fatty acids, aromatic and alicyclic starter units, and ␣-keto and ␣-hydroxy acids
Summary
Natural products represent the basis for the majority of small molecule drugs currently in clinical use, due in part to their diverse and unique chemical scaffolds [1]. Despite great success in the past, bioactivity-guided screening of microbial extracts for natural product discovery is increasingly met with failure characterized by high rediscovery rates [3]. These screening outcomes are at odds with genomic analysis, which suggests that as few as 10% of genetically encoded secondary metabolites are known [4]. A major impediment to genome-guided natural product discovery is the need for accurate methodologies to translate biosynthetic gene sequences into useful chemical information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
