ABSTRACT Proteins are used by scientists to serve a variety of purposes in clinical practice and laboratory research. To optimize proteins for greater function, a variety of techniques have been developed. For the development of reporter genes used in Magnetic Resonance Imaging (MRI) based on Chemical Exchange Saturation Transfer (CEST), these techniques have encountered a variety of challenges. Here we develop a mechanism of protein optimization using a computational approach known as “genetic programming”. We developed an algorithm called Protein Optimization Evolving Tool (POET). Starting from a small library of literature values, use of this tool allowed us to develop proteins which produce four times more MRI contrast than what was previously state-of-the-art. Next, we used POET to evolve peptides that produced CEST-MRI contrast at large chemical shifts where no other known peptides have previously demonstrated contrast. This demonstrated the ability of POET to evolve new functions in proteins. 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. This suggests that unlike existing algorithms for protein engineering that rely on divergent evolution, POET relies on convergent evolution.

Protein Optimization Evolving Tool (POET) based on Genetic Programming