SeMPI 2.0—A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases

Abstract

Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening.

Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs

“…Based on the domains for substrate activation of the BGCs [257], those likely to produce alkaloids, i.e., the NRPS and NRPS/PKS clusters, are easily identified [258], and represent a significant opportunity [249,259,260]. The identification of BGCs having unrecognizable modular characteristics probably indicates new scaffolds [261][262][263] and the characterization of new alkaloid structures [264][265][266][267][268]. The profound challenge is to activate the pathways selectively, or in a heterologous host [260,[269][270][271][272][273].…”

Mining and unearthing hidden biosynthetic potential

“…Thus, the output is likely biased toward common biosynthetic principles and may fail to detect novel pathways. To overcome this limitation, machine learning-based approaches and deep learning strategies were developed that show an improved ability to identify biosynthetic gene clusters (BGCs) of novel classes [17][18][19][20][21] .…”

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

“…The lecture topics included presentations of both novel computational methodologies as well as recent results, e.g., methods for clustering of specialized metabolites and the introduction of a large integrated and open database for NPs ( https://coconut.naturalproducts.net ) and the most recent version of the NuBBE natural products database from Brazil. Other speakers presented web servers for prediction of metabolites from gene cluster data, e.g., the SeMPI 2.0 web server ( http://sempi.pharmazie.uni-freiburg.de/ ) presented by Paul Zierep for polyketide synthase (PKS) and non-ribosomal peptide synthase (NRPS) prediction by combining with metabolite screening in natural product databases [ 22 ] or drug discovery platforms, e.g. that of the University of West Cape (South Africa) presented by Samuel A. Egieyeh.…”