Dataset Information

Bioinformatics-Guided Discovery of Biaryl-Tailored Lasso Peptides.

ABSTRACT: Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) that feature an isopeptide bond and a distinct lariat fold. A growing number of secondary modifications have been described that further decorate lasso peptide scaffolds. Using genome mining, we have discovered a pair of lasso peptide biosynthetic gene clusters (BGCs) that include cytochrome P450 genes. Here, we report the structural characterization of two unique examples of (C-N) biaryl-containing lasso peptides. Nocapeptin A, from Nocardia terpenica, is tailored with Trp-Tyr crosslink while longipepetin A, from Longimycelium tulufanense, features Trp-Trp linkage. Besides the unusual bicyclic frame, longipepetin A receives an S-methylation by a new Met methyltransferase resulting in unprecedented sulfonium-bearing RiPP. Our bioinformatic survey revealed P450(s) and further maturating enzyme(s)-containing lasso BGCs awaiting future characterization.

SUBMITTER: Saad H

PROVIDER: S-EPMC10028836 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Bioinformatics-Guided Discovery of Biaryl-Tailored Lasso Peptides.

Saad Hamada H Majer Thomas T Bhattarai Keshab K Lampe Sarah S Nguyen Dinh T DT Kramer Markus M Straetener Jan J Brötz-Oesterhelt Heike H Mitchell Douglas A DA Gross Harald H

bioRxiv : the preprint server for biology 20230306

Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) that feature an isopeptide bond and a distinct lariat fold. A growing number of secondary modifications have been described that further decorate lasso peptide scaffolds. Using genome mining, we have discovered a pair of lasso peptide biosynthetic gene clusters (BGCs) that include cytochrome P450 genes. Here, we report the structural characterization of two unique examples of (C-N) biaryl-con ...[more]

PMID: 36945544

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Project description:BackgroundIdentifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively.ResultsWe propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks.ConclusionsThe method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

Dataset Information

Bioinformatics-Guided Discovery of Biaryl-Tailored Lasso Peptides.

Publications

Bioinformatics-Guided Discovery of Biaryl-Tailored Lasso Peptides.

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