A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

Hannigan, G. D.; Prihoda, D.; Palicka, A.; Soukup, J.; Klempir, O.; Rampula, L.; Durcak, J.; Wurst, M.; Kotowski, J.; Chang, D.; Wang, R.; Piizzi, G.; Hazuda, D. J.; Woelk, C. H.; Bitton, D. A.

doi:10.1101/500694

bioRxiv · 10.1101/500694

A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

Hannigan, G. D.·Prihoda, D.·Palicka, A.·Soukup, J.·Klempir, O.·Rampula, L.·Durcak, J.·Wurst, M.·Kotowski, J.·Chang, D.·Wang, R.·Piizzi, G.·Hazuda, D. J.·Woelk, C. H.·Bitton, D. A.

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Abstract

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward for in-silico BGC identification.

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Hannigan, G. D., Prihoda, D., Palicka, A., Soukup, J., Klempir, O., Rampula, L., Durcak, J., Wurst, M., Kotowski, J., Chang, D., Wang, R., Piizzi, G., Hazuda, D. J., Woelk, C. H., Bitton, D. A.. 2018-12-18. A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction. https://doi.org/10.1101/500694

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