Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters

Abstract Background A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified functionally annotated based on their DNA sequence. Candidates for further research development may prioritized properties such as functional annotation, (dis)similarity to known BGCs, bioactivity assays. Production the target compound native strain often not achievable, rendering heterologous expression an optimized host a promising alternative. Genome-scale metabolic models are frequently used guide development, but large-scale incorporation testing production complex natural products this framework hampered amount manual work required translate pathways. To end, we have developed pipeline automated reconstruction BGC associated pathways responsible synthesis non-ribosomal peptides polyketides, two dominant classes compounds. Results The correctly predicts 72.8% reactions detailed evaluation 8 different comprising 228 domains. By introducing reconstructed into genome-scale model demonstrate that level accuracy sufficient make reliable silico predictions with respect rate knockout targets. Furthermore, apply large database reconstruct 943 We identify 17 using high-throughput assessment potential targets increasing any However, only provide relative increase up 6% compared wild-type rates. Conclusion With pave way extended use design hosts. In context, generic increased predicted minor single-reaction targets, these results indicate more sophisticated strain-engineering strategies necessary efficient