Using Genetic Programming to Predict and Optimize Protein Function

Abstract

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.

Protein Optimization Evolving Tool (POET) based on Genetic Programming

“…Across 5,000 to 50,000 iterations of this algorithm ( Fig 2. arrow ), the motif-weight pairs that are most important and accurate at predicting the training dataset are maintained, and those that are poor at improving the training dataset are discarded, causing the model to develop in an analogous manner to Darwinian evolution 25 .…”