Abstract
Science is slowly unlocking the secrets of the exquisite chemical synthesis capabilities of polyketide synthases (PKSs), as well as other secondary metabolites’ biosynthesis pathways, and learning to re-engineer such pathways to synthesize novel chemical compounds. Research over the last 30 years has involved innovative experiments and bioinformatics focused on a wide range of medicinal compounds ranging from antibiotics to anticholesterol agents. Furthermore, it has been possible to manipulate PKSs to produce novel compounds for pharmaceutical use. However, despite great progress, our knowledge is still sketchy, and experiments continue to be time-consuming and difficult. PKSs, and secondary metabolite biosynthetic pathways in general, provide model systems for developing and testing experimental and bioinformatic tools for synthetic biology application. Bioinformatic and molecular modelling are important for making sense of existing and future experimental data. Bioinformatic and structural modelling can help in several ways: by predicting how manipulations of protein domains might yield viable novel biosynthetic pathways to generate variants of existing chemicals/pharmaceuticals of high value or to allow the synthesis of totally novel compounds, by assisting the discovery of novel gene clusters in genomic and metagenomic data, by predicting the metabolites synthesized by novel gene clusters and by interpreting experimental data to elucidate the rules governing polyketide synthase function, which feeds back into the others on this list.
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Acknowledgements
RF thanks the Darwin Trust of Edinburgh for financial support and Sam Higginbottom University of Agriculture, Technology and Sciences, India, for study leave and financial support. PJW and CMT thank the BBSRC/EPSRC for support via grant BB/F014570/1.
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Farmer, R., Thomas, C.M., Winn, P.J. (2018). Modelling Polyketide Synthases and Similar Macromolecular Complexes. In: Wadhwa, G., Shanmughavel, P., Singh, A., Bellare, J. (eds) Current trends in Bioinformatics: An Insight. Springer, Singapore. https://doi.org/10.1007/978-981-10-7483-7_7
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