Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Kathrin Leppek; Gun Woo Byeon; Wipapat Kladwang; Hannah K Wayment-Steele; Craig H Kerr; Adele F Xu; Do Soon Kim; Ved V Topkar; Christian Choe; Daphna Rothschild; Gerald C Tiu; Roger Wellington-Oguri; Kotaro Fujii; Eesha Sharma; Andrew M Watkins; John J Nicol; Jonathan Romano; Bojan Tunguz; Fernando Diaz; Hui Cai; Pengbo Guo; Jiewei Wu; Fanyu Meng; Shuai Shi; Eterna Participants; Philip R Dormitzer; Alicia Solórzano; Maria Barna; Rhiju Das

doi:10.1038/s41467-022-28776-w

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Nat Commun. 2022 Mar 22;13(1):1536. doi: 10.1038/s41467-022-28776-w.

Authors

Kathrin Leppek^#¹, Gun Woo Byeon^#¹, Wipapat Kladwang^#², Hannah K Wayment-Steele^#³, Craig H Kerr^#¹, Adele F Xu¹, Do Soon Kim², Ved V Topkar⁴, Christian Choe⁵, Daphna Rothschild¹, Gerald C Tiu¹, Roger Wellington-Oguri⁶, Kotaro Fujii¹, Eesha Sharma², Andrew M Watkins², John J Nicol⁶, Jonathan Romano^{6

7}, Bojan Tunguz^{2

8}, Fernando Diaz⁹, Hui Cai⁹, Pengbo Guo⁹, Jiewei Wu⁹, Fanyu Meng⁹, Shuai Shi⁹, Eterna Participants⁶, Philip R Dormitzer^{9

10}, Alicia Solórzano⁹, Maria Barna¹¹, Rhiju Das^{12

13

14}

Affiliations

¹ Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
² Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA.
³ Department of Chemistry, Stanford University, Stanford, CA, 94305, USA.
⁴ Program in Biophysics, Stanford University, Stanford, CA, 94305, USA.
⁵ Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
⁶ Eterna Massive Open Laboratory, Stanford University, Stanford, CA, 94305, USA.
⁷ Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, New York, 14260, USA.
⁸ NVIDIA Corporation, 2788 San Tomas Expy, Santa Clara, CA, 95051, USA.
⁹ Pfizer Vaccine Research and Development, Pearl River, NY, USA.
¹⁰ GlaxoSmithKline, 1000 Winter St., Waltham, MA, 02453, USA.
¹¹ Department of Genetics, Stanford University, Stanford, CA, 94305, USA. mbarna@stanford.edu.
¹² Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA. rhiju@stanford.edu.
¹³ Program in Biophysics, Stanford University, Stanford, CA, 94305, USA. rhiju@stanford.edu.
¹⁴ Eterna Massive Open Laboratory, Stanford University, Stanford, CA, 94305, USA. rhiju@stanford.edu.

^# Contributed equally.

Abstract

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

COVID-19* / therapy
Humans
Pseudouridine / metabolism
RNA Stability / genetics
RNA*
RNA, Messenger / metabolism

Substances

RNA, Messenger
Pseudouridine
RNA

Abstract

Publication types

MeSH terms

Substances

Grants and funding