An integrated computational framework for protein engineering and novel biocatalyst design

背景资料：在过去的几十年里，塑料产量大幅增长，全球只有五分之一的塑料被回收利用，加拿大的回收利用率不到10%。不断增长的需求要求有效和环保的塑料降解解决方案。基于酶的生物催化剂仍然是各种工业和绿色化学应用的有吸引力的生态友好解决方案，包括清洁和可再生能源的生产以及污染物的生物修复。虽然有益，但天然酶通常在苛刻的工业条件下表现不佳，这阻碍了生物催化剂的大规模转化。因此，对先天酶进行工程改造以优化其物理化学性质和在次优条件下的催化效力是研究的关键途径。 虽然经典方法如定向进化、随机诱变广泛用于工程化，但鉴定用于修饰的合适热点涉及对巨大蛋白质序列空间和无数突变的取样。此外，重要的是获得酶的机制的基础知识，以开发一种有效的生物催化剂。随着高性能计算机和复杂软件技术的快速发展，计算方法目前适合于承担这些挑战。目的：该研究计划旨在开发一个计算框架，整合生物信息学，生物基因组学，先进的原子模型，分子模拟和机器学习，以了解酶的进化，结构-功能关系，以指导靶向蛋白质工程和生物催化剂设计。我和我的研究团队将特别关注工程化聚对苯二甲酸乙二醇酯（PET）水解酶（或P-HE），如PET水解酶（PETase），对苯二甲酸单羟乙酯水解酶（MHETase）和角质酶，用于PET塑料的回收。我们的具体目标是：（O 1）表征P-HE的结构、分子识别机制和催化剂（O2）重建祖先状态以理解分子进化并指导P-HE的工程化和（O3）探索底物通道并在计算机设计的P-HE中进行生物化学验证。影响：这项研究将提供原始知识，在基础生物科学和化学生物学，提高我们的理解的结构-功能的P-HEs。我们的研究将为底物结合、PET的酶水解、修饰热点和工程化P-HE提供新的分子水平见解，这些见解可能有助于开发最佳催化剂，以应对塑料废物即将面临的挑战。该计划将培养下一代科学家将计算技术应用于蛋白质工程。该项目的长期目标是开发一种新型、稳定、高效的塑料生物降解催化剂。我们的研究还将为设计生物催化剂提供一个通用的计算框架。

Background: Plastic production has grown significantly over the last few decades and only a fifth of it is being recycled globally with less than 10% recycled in Canada. The growing demand calls for an effective and eco-friendly solution to plastic degradation. Enzyme-based biocatalysts remain an attractive, ecofriendly solution for various industrial and green chemistry applications, including the production of clean and renewable energies and bioremediation of pollutants. While beneficial, native enzymes often underperform within harsh industrial conditions which hinders the large-scale translation of biocatalysts. Therefore, the engineering of innate enzymes to optimize their physicochemical properties and catalytic potencies for suboptimal conditions is a critical avenue of research. While classic methods such as directed evolution, random mutagenesis are widely used for engineering, identifying the suitable hotspots for modification involves the sampling of vast protein sequence space and a myriad of mutations. Further, it is important to gain fundamental knowledge on the mechanisms of enzymes to develop an efficient biocatalyst. With the rapid advancements of high-performance computers and sophisticated software technologies, computational methods are currently suited to take on these challenges. Objectives: This research program aims at developing a computational framework that integrates bioinformatics, phylogenomics, advanced atomistic modelling, molecular simulation, and machine learning to understand the evolution, structure-function relationships of enzymes for guiding targeted protein engineering and biocatalyst design. My research team and I will particularly focus on engineering the polyethylene terephthalate (PET)-hydrolyzing enzymes (or P-HEs) such as PET hydrolase (PETase), monohydroxyethyl terephthalate hydrolase (MHETase), and cutinase for recycling of PET plastics. Our specific objectives are: (O1) Characterize the structures, molecular recognition mechanisms and catalyses of P-HEs (O2) Reconstruct ancestral states to understand molecular evolution and guide engineering of P-HEs and (O3) Explore substrate channeling and biochemically validate in silico-designed P-HEs. Impact: This research will provide original knowledge in basic bioscience and chemical biology by enhancing our understanding of the structure-functions of P-HEs. Our research will provide novel molecular-level insights into substrate binding, enzymatic hydrolyses of PET, hotspots for modifications, and engineered P-HEs that could be useful for developing an optimal catalyst for tackling the impending challenges from plastic waste. This program will train next-generation scientists in applying computational techniques for protein engineering. The long-term goal of this program is to develop a novel, stable and efficient biocatalyst for plastic biodegradation. Our research will also offer a versatile computational framework for designing biocatalysts.