Speaker
Description
Generative probabilistic models have shown promise in designing artificial RNA and
protein sequences but often suffer from high rates of false positives, where sequences
predicted as functional fail experimental validation. To address this critical limita-
tion, we explore the impact of reintegrating experimental feedback into the model
design process. We propose a likelihood-based reintegration scheme, which we test
through extensive computational experiments on both RNA and protein datasets, as
well as through wet-lab experiments on the self-splicing ribozyme from the group I
intron RNA family where our approach demonstrates particular efficacy. We show
that integrating recent experimental data enhances the model’s capacity of generat-
ing functional sequences (e.g. from 6.7% to 63.7% of active designs at 45 mutations).
This feedback-driven approach thus provides a significant improvement in the design
of biomolecular sequences by directly tackling the false-positive challenge.
| Role | Master/PhD student |
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