Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.
Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.
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通过天然酶序列的生成模型增强荧光素酶活性和稳定性。
DOI：
10.1101/2023.09.18.558367
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Warshel,Arieh
中科院分区：
文献类型：
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作者：
Xie,WenJun;Liu,Dangliang;Wang,Xiaoya;Zhang,Aoxuan;Wei,Qijia;Nandi,Ashim;Dong,Suwei;Warshel,Arieh
天然蛋白质序列与生成AI协同作用的可用性为工程酶提供了新的范例。虽然已经使用生成模型设计了具有许多突变的活性酶变体，但它们的性能通常低于它们的野生型对应物。此外，在实际应用中，选择可以与广泛序列改变的功效相媲美的较少突变通常更有利。确定有益的单突变仍然是一项艰巨的任务。本研究利用生成最大熵模型对Renillaluciferase（RLuc）同源物进行分析，并结合生物化学实验，证明自然进化信息可以通过分别改造活性中心和蛋白质支架来预测性地提高酶的活性和稳定性。提高所设计的单突变体的荧光素酶活性或稳定性的成功率为~ 50%。这一发现突出了自然界进化熟练酶的巧妙方法，其中不同的进化压力优先应用于酶的不同区域，最终达到整体高性能。我们还揭示了RLuc对发射蓝光的进化偏好，与其他光谱相比，该蓝光在水渗透方面具有优势。总之，我们的方法有利于通过酶序列空间导航，并提供了有效的策略，计算机辅助合理的酶工程。
The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyzeRenillaluciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.