Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides
Problem
Biosynthetic gene clusters for new natural product classes escape attention in genome sequences; known RiPP subclasses may be only the tip of an iceberg.
Why it matters
Microbial natural products can have antibiotic, antiviral, or anticancer properties of clinical interest.
What the AI did
decRiPPter combines a Support Vector Machine that identifies candidate RiPP precursors with pan-genomic analyses to mine genomes for novel RiPP classes.
Results
Uncovers a novel class of lanthipeptides (per title); abstract truncated before detailed results
Impact
Evidence supports discovery of a novel lanthipeptide class via the AI-integrated tool, peer-reviewed in PLoS Biology.
Record
| Venue | PLoS Biology |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Alexander Kloosterman, Peter Cimermančič, Somayah S. Elsayed, Chao Du, Michalis Hadjithomas, Mohamed S. Donia, Michael Andrew Fischbach, Gilles P. van Wezel et al. |
| Citations | 127 (OpenAlex, at import time) |
| References | 112 |
| Model or method | decRiPPter (SVM plus pan-genomics) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1371/journal.pbio.3001026 |
| Open access | gold |
| Keywords | RiPP, genome mining, SVM, lanthipeptides, pan-genomics, natural products |
Importance rating
significant — thousands to millions affected
Abstract states microbial natural products can have antibiotic, antiviral, or anticancer properties of clinical interest; tool found 42 new candidate RiPP families across 1,295 genomes.