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.

翻译：蛋白质工程师通常使用引导进化等工具来寻找功能和特性更好的新蛋白。最近,我们聘用了计算技术,特别是机器学习方法来帮助引导进化,展示了有希望的结果。在本文中,我们建议采用基于进化计算方法的计算基因规划工具POET,以加强导进化中的筛选和诱变,并帮助蛋白质工程师找到功能更好的蛋白。作为概念的证明,我们使用了通过化学交换饱和性转移机制检测到的产生MRI对比的peptides。介绍了POET使用的进化方法,并研究了我们化学交换饱和性转移实验不同时代的POET性能。我们的结果表明,像POET这样的计算模型工具可以帮助找到比以前使用的更好400%的功能。