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NIHNATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESR01

Developing mRNAdesigner tool package for optimization of mRNA sequence

Active
Institution
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON, TX
Principal investigator
Xiaobo Zhou
Funding
$312K
Duration
2024–2028
Open portfolio

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Abstract

Abstract The goal of this project is to develop a platform called mRNAdesigner, an integrated deep learning model to optimize 5'UTR, codon usage, and 3'UTR at same time, which enables users to design the optimal mRNA sequence to enhance protein expression level, thus to improve the mRNA translation efficiency mRNA medicines have several beneficial features: safety, efficacy, production, and speed. It is a significant challenge for all of the researchers to maximize the translation efficiency of mRNA vaccine. Multiple factors are involved to regulate the stability and efficiency of mRNA, including 5' untranslated region (UTR), 3' UTR, codon et al, and several in silico approaches have been developed to optimize these factors respectively However, as these factors always function together during the translation of mRNA, and individual optimization is insufficient. Thus, a novel integrated deep learning model for these factors is needed to comprehensively enhance the stability and efficiency of mRNA medicine. We propose to develop a platform mRNAdesigner that will address the challenges in design of mRNA sequence to enhance mRNA stability and translation efficiency, and thus provide important information for the development and optimization of mRNA vaccines. mRNAdesigner includes three components: mRNAbase, mRNAoptimizer, and mRNAviewer. mRNAbase is a database to collect high translational efficiency gene data, the optimized mRNA sequence of ORF, 5' UTR and 3'UTR for any target proteins, the energy, secondary structure, and functional annotation of those optimized mRNA sequence of ORF; mRNAoptimizer, in silico deep learning optimization of mRNA sequence, provides a fast methodology to investigate all possible integration of the ORF, 5' UTR and 3'UTR and identify the optimal mRNA sequence; and mRNAviewer to visualize the optimized mRNA sequences, the secondary structures and the related functions. Finally, the integrated models by designing the 5'UTR, codon, and 3'UTR sequence for representative will be experimentally validate in vitro and in vivo. Software prototypes mRNAdesigner including mRNAbase, mRNAoptimizer, and mRNAviewer will be made publicly available to the research community via a project website at https:/ccsm.uth.edu/NSF-mRNAdesigner.

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R01NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESBiodata Management and Analysis Study Section[BDMA]sequenceefficiencydeep learningsilicocodonmrnautrlearning model

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