Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning.

ABSTRACT: mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accurac

SUBMITTER: Cetnar DP 

PROVIDER: S-EPMC11541907 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature