Nature CommunicationsBiochemistryPeer-reviewed journal article
Expanding the space of self-reproducing RNAs using probabilistic generative models
Camille N. LambertiD, Vaitea OpuuiD, Francesco CalvaneseiD, Polina PavlinovaiD, Francesco ZamponiiD, Eric J. HaydeniD, Martin WeigtiD, Matteo SmerlakiD, Philippe NgheiD
Abstract
Estimating the plausibility of RNA self-reproduction is central to origin-of-life scenarios but self-reproduction has been shown in only a handful of systems. Here, we populated a vast sequence space of ribozymes using statistical covariation models and secondary structure prediction. Experimentally assayed sequences were found active as far as 65 mutations from a reference natural sequence. The number of potentially generated sequences together with the experimental success rate indicate that at least [~]1039 such ribozymes may exist. Randomly sampled artificial ribozymes exhibited autocatalytic self-reproduction akin to the reference sequence. The combination of high-throughput screening and probabilistic modeling considerably improves our estimation of the number of self-reproducing systems, paving the way for a statistical approach to the origin of life.
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: protein folding & dynamics
- approach: statistical model
- approach: deep learning
- Citations
- 13
- FWCI
- 2.80
- Field percentile
- top 9%
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