Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts.

With advances in sequencing technology, uncharacterized proteins and domains of unknown function (DUFs) are rapidly accumulating in sequence databases and offer an opportunity to discover new protein chemistry and reaction mechanisms. The focus of this review, the formerly enigmatic YcaO superfamily (DUF181), has been found to catalyze a unique phosphorylation of a ribosomal peptide backbone amide upon attack by different nucleophiles. Established nucleophiles are the side chains of Cys, Ser, and Thr which gives rise to azoline/azole biosynthesis in ribosomally synthesized and posttranslationally modified peptide (RiPP) natural products. However, much remains unknown about the potential for YcaO proteins to collaborate with other nucleophiles. Recent work suggests potential in forming thioamides, macroamidines, and possibly additional post-translational modifications. This review covers all knowledge through mid-2016 regarding the biosynthetic gene clusters (BGCs), natural products, functions, mechanisms, and applications of YcaO proteins and outlines likely future research directions for this protein superfamily.

Recently developed bioinformatic tools have bolstered the discovery of ribosomally synthesized and posttranslationally modified peptides (RiPPs). Using an improved version of Rapid ORF Description & Evaluation Online (RODEO 2.0), a biosynthetic gene cluster mining algorithm, we bioinformatically mapped the sactipeptide RiPP class via the radical S-adenosylmethionine (SAM) enzymes that form the characteristic sactionine (sulfur-to-alpha carbon) crosslinks between cysteine and acceptor residues. Hundreds of new sactipeptide biosynthetic gene clusters were uncovered and a novel sactipeptide "huazacin" with growth-suppressive activity against Listeria monocytogenes was characterized. Bioinformatic analysis further suggested that a group of sactipeptide-like peptides heretofore referred to as SCIFFs (six cysteines in forty-five residues) might not be sactipeptides as previously thought. Indeed, the bioinformatically-identified SCIFF

Thiopeptides are members of the ribosomally synthesized and post-translationally modified peptide family of natural products. Most characterized thiopeptides display nanomolar potency toward Gram-positive bacteria by blocking protein translation with several being produced at the industrial scale for veterinary and livestock applications. Employing our custom bioinformatics program, RODEO, we expand the thiopeptide family of natural products by a factor of four. This effort revealed many new thiopeptide biosynthetic gene clusters with products predicted to be distinct from characterized thiopeptides and identified gene clusters for previously characterized molecules of unknown biosynthetic origin. To further validate our data set of predicted thiopeptide biosynthetic gene clusters, we isolated and characterized a structurally unique thiopeptide featuring a central piperidine and rare thioamide moiety. Termed saalfelduracin, this thiopeptide displayed potent antibiotic activity toward several drug-resistant Gram-positive pathogens. A combination of whole-genome sequencing, comparative genomics, and heterologous expression experiments confirmed that the thioamide moiety of saalfelduracin is installed post-translationally by the joint action of two proteins, TfuA and YcaO. These results reconcile the previously unknown origin of the thioamide in two long-known thiopeptides, thiopeptin and Sch 18640. Armed with these new insights into thiopeptide chemical-genomic space, we provide a roadmap for the discovery of additional members of this natural product family.