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Secondary metabolite biosynthetic pathway software tools | Drug discovery data analysis

Natural products of bacteria and fungi are the most important source for antimicrobial drug leads. For decades, such compounds were exclusively found by chemical/bioactivity-guided screening approaches. The rapid progress in sequencing technologies only recently allowed the development of novel screening methods based on the genome sequences of potential producing organisms. The basic principle of such genome mining approaches is to identify genes, which are involved in the biosynthesis of such molecules, and to predict the products of the identified pathways. Thus, bioinformatics methods and tools are crucial for genome mining.

Source text:
(Weber, 2014) In silico tools for the analysis of antibiotic biosynthetic pathways. Int J Med Microbiol.

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antiSMASH / antibiotics & Secondary Metabolite Analysis Shell
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Aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters. antiSMASH facilitates the mining of bacterial and fungal genomes. It includes gene cluster boundary prediction for fungal biosynthetic gene clusters (BGCs), improved chemistry predictions for terpene, ribosomal peptide and non-ribosomal peptide BGCs, comparative alignment of trans-AT polyketide synthase (PKS) assembly lines and TTA codon annotation. A user interface was also introduced.
SBSPKS
A webserver for carrying out sequence and structure based analysis of Polyketide Synthases, an important family of multifunctional megasynthases involved in the biosynthesis of a variety of pharmaceutically important secondary metabolites. The knowledge base for development of SBSPKS is derived from a comprehensive bioinformatics analysis of the sequence and structural features of a large number of experimentally characterized PKS gene clusters having known metabolic products and recently available crystal structures of PKS and FAS proteins. SBSPKS is a valuable resource for predicting biosynthetic products of uncharacterized PKS clusters and rational design of engineered polyketides.
PRISM / PRediction Informatics for Secondary Metabolomes
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.
SeMPI
Follows an ab-initio approach and predicts the correct order of the substrates which constitute the secondary metabolite. SeMPI is a web server that successfully combines and complements available polyketide prediction methods with a unique database matching algorithm. This online method provides insights into putative molecule structures, and can help researchers to understand the syntheses steps of the gene clusters much better. Finally, this web server enables researchers to identify promising gene clusters more efficiently and prevent them from investing great efforts in structure determination of a cluster product.
NaPDoS
Detects and analyzes secondary metabolite genes. NaPDoS is especially designed to concern (detection and extraction) C- and KS- domains from DNA or amino acid sequence data. This includes metagenomic data sets, whole genomes, PCR amplicon products, and individual genes. NaPDoS is optimized for the identification and classification of bacterial polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes. This tool enables to identify eukaryotic KS and C domains given their shared evolutionary history with prokaryotic homologs.
ThioFinder
Identifies thiopeptide biosynthetic gene clusters in the user-supplied nucleotide or genomic sequences. ThioFinder is a web-based tool that works with an open-access database named ThioBase. It employs the multiple features of thiopeptide biosynthetic machinery, including the ribosomal precursor peptide and the highly conserved partners for the thiopeptide-specific posttranslational modifications. It utilizes a Hidden Markov Models (HMMs)-based approach to automatically predict thiopeptide biosynthetic gene clusters.
NP.searcher / Natural products search engine
Scans rapidly microbial genomes for secondary metabolite biosynthetic gene clusters, and output candidate nonribosomal peptide and polyketide natural products in SMILES format, enabling immediate decoding of DNA to produce 2D and 3D structures in widely available software. The ability to recognize novel nonribosomal peptide (NRP) and polyketide (PK) products will grow with continuous updating of the search engine's database of adenylation and acyltransferase signature sequences for various amino acid and polyketide starter and extender units.
clusterTools
Aids in the characterization of putative biosynthetic gene clusters (BGCs). clusterTools organizes genomic information on coding sequences in a way that enables directed, hypothesis-driven queries for functional elements in close physical proximity of each other. It can be used to identify interesting BGCs from a database of putative BGCs, or on databases of genomic sequences to identify and download regions of interest in the DNA for further processing and annotation in programs.
FunGeneClusterS
Predict clusters with more than 10 genes and allows identification of co-regulated gene clusters irrespective of the function of the genes. FunGeneClusterS is a web application implemented as a graphical interface to predict co-regulated genes. Additional options allow for a finetuned analysis and larger clusters can be identified using the skip option. This method for gene cluster prediction in fungi based on transcriptome data can predict co-regulated clusters not limited to secondary metabolism.
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