Boosting(机器学习)
聚合酶
DNA聚合酶
DNA
化学
计算生物学
生物
计算机科学
人工智能
生物化学
作者
Yi‐Hao Chen,Mingjun Zhu,Rui Ding,Xiaoling Zhao,Zhiqing Tao,Xu Zhang,Maili Liu,Lichun He
标识
DOI:10.1016/j.mrl.2025.200218
摘要
With rapid developments of emerging technologies like synthetic biology, the demand for DNA polymerases with superior activities including higher thermostability and processivity has increased significantly. Thus, rational optimization of the performance of DNA polymerase is of great interest. Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique used for studying protein structure and dynamics. It provides the atomic resolution information of enzymes under their functional solution environment to reveal the active sites (hot spots) of the enzyme, which could be further used for optimizing the performance of enzymes. In our previous work, we identified hot spot residues of Pyrococcus furiosus DNA polymerase (Pfu pol). We aim to employ these binding hot spots to screen for co-factors of Pfu pol, particularly targeting those molecules exhibiting weak intermolecular interactions. To validate this concept, we first demonstrated the feasibility of utilizing hot spot residues as screening probes for auxiliary factors by employing the well-characterized Tween-20 as a model system. Employing these hot spots as probes, two new co-factors, the heat shock protein TkHSP20 from Thermococcus Kodakaraensis and the chemical chaperone l-arginine, are identified to interact with Pfu pol to boost its performance in amplifying long DNA fragments by enhancing the thermal stability and the processivity of the Pfu pol. This NMR-based approach requires no prior assignment information of target enzymes, guiding the rational exploration of novel co-factors for Pfu pol. Moreover, our approach is not dependent on structural data or bioinformatics. Therefore, it has significant potential for application in various enzymes to expedite the progress in enzyme engineering.
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