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.
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.
Predicts A-domain specificity using Support Vector Machines (SVM) on four hierarchical levels, ranging from gross physicochemical properties of an A-domain’s substrates down to single amino acid substrates. NRPSpredictor can predict the putative substrate specificity on four different hierarchical levels for bacterial A-domains and on one level for fungal A-domains. It can also predict single amino acid specificities.
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.
Facilitates systematic mapping of secondary metabolites (SMs) clusters in fungal genomes. SMURF is a web application that relies on hidden Markov model (HMM) searches to detect backbone genes in sequenced fungal genomes. This online method is based on three hallmarks of fungal SM biosynthetic pathways: (i) the presence of backbone genes, (ii) clustering, and (iii) characteristic protein domain content.
Predicts biosynthetic gene clusters in genomes. The software detects gene clusters irrespective of whether they belong to known or a priori specified classes. It exhibits relatively little training set bias and is capable of identifying new classes of gene clusters.
Predicts secondary metabolite (SM) anchor genes, also called SM backbone genes, in protein sequences. SMIPS is a webserver for genome-wide detection of SM key enzymes ('anchor' genes): polyketide synthases, non-ribosomal peptide synthetases and dimethylallyl tryptophan synthases.
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.