Abstract
BackgroundFungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome.ResultsHere we report synthaser, a Python package leveraging the NCBI’s conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. Synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. Synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application (http://gamcil.github.io/synthaser). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network.ConclusionsSynthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. It is freely available under a MIT license from PyPI (https://pypi.org/project/synthaser) and GitHub (https://github.com/gamcil/synthaser).
Highlights
Domains are distinct functional and structural units that serve as the evolutionary building blocks of proteins
We show synthaser to be a useful addition to the genome mining toolbox, within the context of natural products research; given the programmable nature of synthaser, we can foresee much broader applications of the software
There is significant interest in the genome mining of new polyketide and nonribosomal pathways for their potential in making new drugs [11]. These megasynthases are large enzymes consisting of multiple functional domains, each responsible for a different step in the biosynthesis of the products backbone
Summary
Domains are distinct functional and structural units that serve as the evolutionary building blocks of proteins. Extensive domain rearrangement over time has led to the diversification of existing proteins, as well as the emergence of novel protein families [6] Through this process, domains are placed into new molecular contexts where, via their interactions with different combinations of domains, novel functionality can be birthed [7, 8]. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain archi‐ tecture of the corresponding megasynth(et)ase, and accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome
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