Using Genetic Programming to Predict and Optimize Protein Function

TL;DR

This paper introduces POET, a genetic programming tool that enhances protein screening and mutagenesis, demonstrating significant improvements in peptide functionality prediction for MRI contrast agents.

Contribution

The paper presents a novel genetic programming approach, POET, to improve protein function prediction and optimization, integrating evolutionary computation with protein engineering.

Findings

POET can identify peptides with 400% better functionality.

The method outperforms traditional screening techniques.

POET accelerates the discovery of high-performance proteins.

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.