利用遗传编程预测和优化蛋白质功能
Using Genetic Programming to Predict and Optimize Protein Function
摘要：
蛋白质工程师通常使用定向进化等工具来寻找具有更好功能和特性的新蛋白质。最近，计算技术，特别是机器学习方法已被招募来协助定向进化，显示出有希望的结果。在本文中，我们提出了POET，一个计算遗传编程工具的基础上演化计算方法，以加强筛选和诱变定向进化，并帮助蛋白质工程师找到更好的功能蛋白质。作为一个概念验证，我们使用的肽产生的MRI对比检测的化学交换饱和转移对比机制。介绍了POET中的进化方法，并利用化学交换饱和转移对比实验研究了POET在不同时期的性能。我们的研究结果表明，像POET这样的计算建模工具可以帮助找到比以前使用的功能好400%的肽。
英文摘要：
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.
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