# Optimizing mRNA sequences with deep neural networks

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $351,000

## Abstract

The COVID-19 pandemic has presented new challenges to individuals world-wide. Since the first reports of
infections in the US more than 90 million individuals have become infected and more than 1 million have died.
SARS-CoV-2 genome has various open reading frames (ORFs) encoding 16 non-structural proteins (NSPs), 4
structural proteins and several accessory proteins. The genome of RNA virus can easily generate mutations as
virus spreads. The constant emergence of new mutations in SARS-CoV-2 is the major challenge for the ongoing
development of antiviral drug and broad neutralizing antibodies. The two mRNA vaccines from Pfizer/BioNTech
and Moderna are moderate effective, 45 to 75 percent at protecting people from in preventing infection from the
delta variant, and both of them have received emergency use authorization. More seriously, the omicron variant
was first detected in southern Africa and quickly expanded to the whole world. According to a recent study,
traditional dosing regimens of COVID-19 vaccines available in the US do not produce antibodies capable of
recognizing and neutralizing the Omicron variant. The global data shows the coronavirus pandemic is far from
over. Thus, more variants are expectable and some of them may escape the immune response produced after
vaccination. How to keep the efficacy of existing mRNA vaccines on variants is challenging us. Aside from SARS-
CoV-2, mRNA medicines against cancer and other infectious disease, such as Ebola, Zika virus, and influenza,
are advancing through clinical trials.
 The goal of this project is to develop an integrated deep learning model to optimize 5'UTR, codon usage, and
3'UTR at same time that enables users to design the optimal mRNA sequence to enhance protein expression
level, thus to improve the efficacy of mRNA medicines. mRNA medicines hold great promise for the treatment of
a wide variety of disease, extending from prophylactics to therapeutics for infectious diseases, cancer, and
genetic disease. mRNA medicines have several beneficial features: safety, efficacy, production, and speed.
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 being 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. In silico optimization of mRNA vaccine
provides a fast methodology to investigate all possible integration of the ORF, 5' UTR and 3'UTR and identify
the optimal mRNA vaccine. The interdisciplinary team proposed to develop the following aims: (1) developing
deep learning models for 5' UTR, codon, and 3' UTR respectively, and integrated model for systemic optimization
of 5' UTR, codon, and 3' UTR;...

## Key facts

- **NIH application ID:** 10796505
- **Project number:** 1R01LM014156-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaobo Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $351,000
- **Award type:** 1
- **Project period:** 2024-08-26 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10796505

## Citation

> US National Institutes of Health, RePORTER application 10796505, Optimizing mRNA sequences with deep neural networks (1R01LM014156-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10796505. Licensed CC0.

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