论文标题
使用遗传编程预测和优化蛋白质功能
Using Genetic Programming to Predict and Optimize Protein Function
论文作者
论文摘要
蛋白质工程师通常使用定向进化的工具来找到具有更好功能和性状的新蛋白质。最近，已经招募了计算技术，尤其是机器学习方法，以帮助导向进化，显示出令人鼓舞的结果。在本文中，我们提出了一种基于进化计算方法的计算遗传编程工具，以增强定向进化中的筛选和诱变，并帮助蛋白质工程师找到具有更好功能的蛋白质。作为概念验证，我们使用的肽会产生通过化学交换饱和转移对比机制检测到的MRI对比度。描述了诗人中使用的进化方法，并研究了诗人在我们的化学交换饱和转移对比度的不同时期的表现。我们的结果表明，像诗人这样的计算建模工具可以帮助找到比以前使用的肽的功能。
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.
