A Genetic Programming Approach to Engineering MRI Reporter Genes

Alexander R. Bricco, Iliya Miralavy, Shaowei Bo, Or Perlman, David E. Korenchan, Christian T. Farrar, Michael T. McMahon, Wolfgang Banzhaf*, Assaf A. Gilad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Here we develop a mechanism of protein optimization using a computational approach known as “genetic programming”. We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

Original languageEnglish
Pages (from-to)1154-1163
Number of pages10
JournalACS Synthetic Biology
Volume12
Issue number4
DOIs
StatePublished - 21 Apr 2023

Funding

FundersFunder number
National Institutes of Health
National Institute of Diabetes and Digestive and Kidney DiseasesNSF 2027113, S10-OD023406, R01-DK121847
National Institute of Neurological Disorders and StrokeR01-NS098231
National Institute of Biomedical Imaging and BioengineeringR01-EB030565, P41-EB024495, R01-EB031008, R01-EB031936

    Keywords

    • CEST MRI
    • genetic programming
    • protein engineering

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