Improving protein solubility and activity by introducing small peptide tags designed with machine learning models
Improving protein solubility and activity by introducing small peptide tags designed with machine learning models
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DOI：
10.1016/j.mec.2020.e00138
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发表时间：
2020-12-01
影响因子：
5.2
通讯作者：
Zhou, Kang
中科院分区：
文献类型：
--
作者：
Han, Xi;Ning, Wenbo;Zhou, Kang
提高酶的催化能力对于许多代谢工程项目的成功至关重要，但可能的蛋白质突变体的搜索空间太大，无法通过实验进行详尽的探索。在某种程度上，高溶解度酶由于其更好的折叠质量而往往表现出高活性。在这里，我们证明基于回归模型的优化算法可以有效地设计短肽标签以提高一些模型酶的溶解度。基于蛋白质序列信息，我们最近开发的支持向量回归模型用于评估将小肽标签引入目标蛋白质后的蛋白质溶解度。优化算法引导标签序列向具有更高溶解度的变体进化。通过测量带有和不带有识别标签的模型酶的溶解度和活性，成功验证了优化结果。一种蛋白质（酪氨酸解氨酶）的溶解度增加了一倍多，活性提高了250%。该策略成功地增加了我们测试的另外两种酶（乙醛脱氢酶和 1-脱氧-D-木酮糖-5-磷酸合酶）的溶解度。因此，所提出的优化方法为提高代谢工程和其他生物技术项目的酶性能提供了有价值的工具。
Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubility. The optimization results were validated successfully by measuring solubility and activity of the model enzyme with and without the identified tags. The solubility of one protein (tyrosine ammonia lyase) was more than doubled and its activity was improved by 250%. This strategy successfully increased solubility of another two enzymes (aldehyde dehydrogenase and 1-deoxy-D-xylulose-5-phosphate synthase) we tested. The presented optimization methodology thus provides a valuable tool for improving enzyme performance for metabolic engineering and other biotechnology projects.