# Engineering Enzymes for Improved Stability and Retained Function via Rapid Design-Build-Test-Learn Cycles integrating AI/Physics based predictions with Cell-free Protein Synthesis Experimental Testing

> **NIH NIH R15** · BRIGHAM YOUNG UNIVERSITY · 2024 · $448,723

## Abstract

Abstract
The transformative breakthrough of Google DeepMind’s AlphaFold2 on the reliability of sequence to protein
structure prediction, demonstrated the power of machine learning approaches in advancing the study and
engineering of proteins. Currently a number of inverse protein folding neural network models employ different
objective functions in the design of proteins which come with trade-offs and can lead to adversarial sequence
predictions. This project seeks to apply a different objective function to overcome limitations of current inverse
protein folding models with the specific goal of predicting mutations that will increase the stability of therapeutic
and diagnostic proteins. Additionally, AI- and Physics-based Simulation filters are integrated to enable the
prediction of sequences that increase stability and retain function. It is hypothesized that by combining these AI
tools with the experimental cell-free protein synthesis and stability/activity assays, rapid design-build-test-learn
cycles can be performed to create AI models tuned specifically for the target protein. This technology is directly
applied to the highly sensitive diagnostic reporter protein NanoLuc and the promising cancer therapeutic
Onconase to expand their utility through enhanced stability.

## Key facts

- **NIH application ID:** 10973759
- **Project number:** 1R15GM155803-01
- **Recipient organization:** BRIGHAM YOUNG UNIVERSITY
- **Principal Investigator:** Dennis Della Corte
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $448,723
- **Award type:** 1
- **Project period:** 2024-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10973759

## Citation

> US National Institutes of Health, RePORTER application 10973759, Engineering Enzymes for Improved Stability and Retained Function via Rapid Design-Build-Test-Learn Cycles integrating AI/Physics based predictions with Cell-free Protein Synthesis Experimental Testing (1R15GM155803-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10973759. Licensed CC0.

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