  Protein engineers conventionally use tools such as Directed Evolution to find
new proteins with better functionalities and traits. More recently,
computational techniques and especially machine learning approaches have been
recruited to assist Directed Evolution, showing promising results. In this
paper, we propose POET, a computational Genetic Programming tool based on
evolutionary computation methods to enhance screening and mutagenesis in
Directed Evolution and help protein engineers to find proteins that have better
functionality. As a proof-of-concept we use peptides that generate MRI contrast
detected by the Chemical Exchange Saturation Transfer contrast mechanism. The
evolutionary methods used in POET are described, and the performance of POET in
different epochs of our experiments with Chemical Exchange Saturation Transfer
contrast are studied. Our results indicate that a computational modelling tool
like POET can help to find peptides with 400% better functionality than used
before.
