PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

By M. Skinnider, Nishanth J. Merwin, Chad W. Johnston, N. Magarvey. Nucleic Acids Res., 2017

296 citations recorded by the source corpus.

Abstract Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/ab75897c7e7d6e57d6af4f8e8245405d9079224e

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1. Oscar P. Kuipers
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Citations in the corpus

• BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins (2018, methodology): “Since the development of the first version of BAGEL (8) more web servers have been developed such as antiSMASH (9), PRISM (10) and recently RIPPMiner (11).”
• A deep learning genome-mining strategy for biosynthetic gene cluster prediction (2019, methodology): “One such widely used machine learning approach named ClusterFinder (16) employs a Hidden Markov Model (HMM) instead of the multiple sequence alignment based profile-HMM (17) methods seen in other approaches such as AntiSMASH (ANTIbiotics & Secondary Metabolite Analysis SHell) (14) and PRISM (18).”