TY - JOUR
AU - Wayment-Steele, Hannah K.
AU - Kladwang, Wipapat
AU - Watkins, Andrew M.
AU - Kim, Do Soon
AU - Tunguz, Bojan
AU - Reade, Walter
AU - Demkin, Maggie
AU - Romano, Jonathan
AU - Wellington-Oguri, Roger
AU - Nicol, John J.
AU - Gao, Jiayang
AU - Onodera, Kazuki
AU - Fujikawa, Kazuki
AU - Mao, Hanfei
AU - Vandewiele, Gilles
AU - Tinti, Michele
AU - Steenwinckel, Bram
AU - Ito, Takuya
AU - Noumi, Taiga
AU - He, Shujun
AU - Ishi, Keiichiro
AU - Lee, Youhan
AU - Öztürk, Fatih
AU - Chiu, King Yuen
AU - Öztürk, Emin
AU - Amer, Karim
AU - Fares, Mohamad Y.
AU - Das, Rhiju
AU - Eterna Participants
PY - 2022
DA - December
TI - Deep learning models for predicting RNA degradation via dual crowdsourcing
JO - Nature Machine Intelligence
EP - 1184
VL - 4
DO - https://doi.org/10.1038/s42256-022-00571-8
AB - Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.
PB - Springer Nature
UR - https://discovery.dundee.ac.uk/en/publications/2c6cc981-f9dd-4889-af95-82673a4930c0
KW - Coronavirus (COVID-19)
ER