RNA language models predict mutations that improve RNA function.

Autor: Shulgina Y; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA., Trinidad MI; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Howard Hughes Medical Institute, University of California, Berkeley, CA, USA., Langeberg CJ; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA., Nisonoff H; Center for Computational Biology, University of California, Berkeley, CA, United States., Chithrananda S; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA., Skopintsev P; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA., Nissley AJ; Department of Chemistry, University of California, Berkeley, CA, USA., Patel J; Innovative Genomics Institute, University of California, Berkeley, CA, USA., Boger RS; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Biophysics Graduate Program, University of California, Berkeley, CA, USA., Shi H; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Howard Hughes Medical Institute, University of California, Berkeley, CA, USA., Yoon PH; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.; Department of Chemistry, University of California, Berkeley, CA, USA., Doherty EE; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA., Pande T; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA., Iyer AM; Department of Physics, University of California, Berkeley, CA, USA., Doudna JA; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA.; Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.; Department of Chemistry, University of California, Berkeley, CA, USA.; MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.; Gladstone Institutes, University of California, San Francisco, CA, USA., Cate JHD; Innovative Genomics Institute, University of California, Berkeley, CA, USA.; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.; California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA.; Department of Chemistry, University of California, Berkeley, CA, USA.; MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2024 Sep 16. Date of Electronic Publication: 2024 Sep 16.
DOI: 10.1101/2024.04.05.588317
Abstrakt: Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data 1 . Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function 2-4 . We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB) 5 . GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.
Competing Interests: Conflict of Interest J.H.C. is founder, board and SAB member of Initial Therapeutics. The Regents of the University of California have patents issued and pending for CRISPR technologies on which J.A.D. is an inventor. J.A.D. is a cofounder of Azalea Theratupics, Caribou Biosciences, Editas Medicine, Evercrisp, Scribe Therapeutics, Intellia Therapeutics, and Mammoth Biosciences. J.A.D. is a scientific advisory board member at Evercrisp, Caribou Biosciences, Intellia Therapeutics, Scribe Therapeutics, Mammoth Biosciences, The Column Group and Inari. J.A.D. is Chief Science Advisor to Sixth Street, a Director at Johnson & Johnson, Altos and Tempus, and has a research project sponsored by Apple Tree Partners. The remaining authors declare no competing interests.
Databáze: MEDLINE
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