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Modular polyketide synthases (PKSs) are large multi-enzymatic, multi-domain megasynthases, which are involved in the biosynthesis of a class of pharmaceutically important natural products, namely polyketides. These enzymes harbor a set of repetitive active sites termed modules and the domains present in each module dictate the chemical moiety that would add to a growing polyketide chain. This modular logic of biosynthesis has been exploited with reasonable success to produce several novel compounds by genetic manipulation. However, for harnessing their vast potential of combinatorial biosynthesis, it is essential to develop knowledge based in silico approaches for correlating the sequence and domain organization of PKSs to their polyketide products. In this work, we have carried out extensive sequence analysis of experimentally characterized PKS clusters to develop an automated computational protocol for unambiguous identification of various PKS domains in a polypeptide sequence. A structure based approach has been used to identify the putative active site residues of acyltransferase (AT) domains, which control the specificities for various starter and extender units during polyketide biosynthesis. On the basis of the analysis of the active site residues and molecular modelling of substrates in the active site of representative AT domains, we have identified a crucial residue that is likely to play a major role in discriminating between malonate and methylmalonate during selection of extender groups by this domain. Structural modelling has also explained the experimentally observed chiral preference of AT domain in substrate selection. This computational protocol has been used to predict the domain organization and substrate specificity for PKS clusters from various microbial genomes. The results of our analysis as well as the computational tools for prediction of domain organization and substrate specificity have been organized in the form of a searchable computerized database (PKSDB). PKSDB would serve as a valuable tool for identification of polyketide products biosynthesized by uncharacterized PKS clusters. This database can also provide guidelines for rational design of experiments to engineer novel polyketides††http://www.nii.res.in/pksdb.html. [Copyright &y& Elsevier]

[Display omitted] • Completely automated prediction of crosslinked chemical structure of RiPPs using genomic sequences of RiPP BGCs. • ML approach for identifying precursor from multiple small ORFs in a RiPP BGC & predicting cleavage and cross-links. • Identification of uncrosslinked modified residues in lanthipeptides and enhancement of prediction accuracy. RiPPMiner-Genome is a unique bioinformatics resource for identifying Biosynthetic Gene Clusters (BGC) for RiPPs (Ribosomally Synthesized and Post-translationally Modified Peptides) and automated prediction of crosslinked chemical structures of RiPPs starting from genomic sequences. It is a major update of the RiPPMiner webserver, which used only peptide sequence of RiPP precursors as input for predicting RiPP class and crosslinked chemical structures. Other major improvements are, machine learning (ML) based identification of correct RiPP precursor peptide from among multiple small ORFs (Open Reading Frames) in a BGC, prediction of the cleavage site and cross-links in thiopeptides and identification of non-crosslinked modified residues in lanthipeptides. It has been benchmarked on a dataset of 204 experimentally characterized RiPP BGCs. RiPPMiner-Genome also facilitates visualization of the RiPP BGCs and depiction of the chemical structure of crosslinked RiPP. It also has an interface for searching characterized RiPPs, similar to the predicted core peptide sequence or crosslinked chemical structure. [ABSTRACT FROM AUTHOR]