(4jv) Design for Enzymes: Toolkits, Biochemistry, and Engineering

With enzymology, bioinformatics, and metabolic engineering, we are becoming great at discovering, understanding, and using complicated enzyme systems. However, how do we effectively evolve them without being trapped in low-energy valleys? How to produce them without using complicated host systems? Moreover, what are the biochemical benefits of forming assemblies? Finally, can we take advantage of their biochemical principles to expand their catalytic scope, or even make synthetic enzyme assemblies? These questions are valuable to answer to extend our biochemical knowledge, and benefit the healthcare and material industry.

To answer these questions, we need to combine expertise from biochemistry, computational protein design, and protein engineering, all disciplines I have been trained in during my Ph.D. (van der Donk lab, bioinformatics and biosynthesis) and in my PostDoc (David Baker Lab, protein design).

Project area 1: Sensor design for high throughput enzyme activity screening. While directed evolution is the gold standard for enzyme activity engineering, it is limited by low screening space (102 – 107) versus the actual enormous space, requirement of a low-activity ancestral enzyme (also see area 3), and tedious screening procedures (often chromatography). If a reporter system can be built for any enzymatic reactions, high throughput(HTP) assays can be poised for any enzyme evolution tasks, which will significantly expand screening spaces and largely simplify screening procedures. Previously, I built a deep learning-based small molecule chemical-induced dimerization (CID) system design pipeline (An et al, 2023) and demonstrated its success in the laboratory. I plan to first focus on designing CID-based sensor systems to allow the evolution of enzymes producing critical therapeutics, such as taxol. I expected to obtain a relatively large size of enzyme mutants-activity relationship dataset, which can be further used for improving computational methods.

Project area 2: Reconstruction of stable, soluble, and functional enzyme assemblies. Biosynthetic enzymes from eukaryotic systems often form assemblies, such as purisome. While we want to understand the biology behind why they form assemblies and use them for drug production, they often cannot be well reconstituted in engineered hosts or in vitro. I plan to build pipelines for constructing stable and soluble enzyme assemblies while maintaining their biological functions. We recently used protein design softwares on enzyme redesign and successfully got many enzymes (even plant enzymes!) to be produce-able in E. coli with high stability while maintaining their activity (Sumida et al, 2023). I plan to use existing machine learning-based algorithms and heterologous expression methods to reconstitute enzyme assemblies in E. coli/yeast while maintaining their biosynthetic activities. This goal will generate stable production microorganisms for value drugs, meanwhile, provide model systems for enzyme assembly biology study. I will first focus on enzyme assemblies for valuable flavonoids. In the long run, I want to study biochemical benefits of enzyme assemblies through breaking their oligomerization state and measuring their corresponding kinetic changes.

Project area 3: Enzyme repurpose through protein design. Directed evolution has allowed the creation of new reactions using parent enzymes. Yet, reactions with no starting activity, involving multiple steps, multiple substrates, relying on multiple enzymatic domains are still challenging/tedious to evolve. I previously successfully showed how to design a protein-ligand binding interface (An et al, 2023). I plan to redesign the enzymatic substrate binding pocket to expand enzyme substrate scopes. I will focus on redesigning streamlined biosynthetic enzymes to produce valuable drug analogs.

With a strong interdisciplinary background on protein design and biosynthesis, I will bring new tools and strategies to study enzyme biochemistry.

Teaching Interests: With the advance of technologies including machine learning, genome sequencing, and gene editing, the biochemistry/bioengineering field is embracing huge opportunities to challenge fundamental science and solving problems with immediate applications. Future professionals will need to receive extensive training on fundamentals and embrace technologies from different fields, such as computer science, and learn the language to communicate with experts from different backgrounds. As an instructor and a mentor, I have been and will keep developing a pedagogy based on two interwoven threads: 1) an curiosity-driven teaching of fundamental knowledges and critical thinkings to set strong expertise and 2) an passion-driven learning of the state-of-the-art technologies from various fields to solve fundamental biochemical problems to encourage problem solving with new science progresses. Each student has their own unique passion and talent, I plan to tailor the execution of my teaching to my mentees, nurture their passion in science, set them ready for solving problems with strong background and critical thinking, encourage them to step out of their comfort zones, learn from and collaborate people from other field, and support them for their future career.

Teaching Experience: I have teaching experience shaped by my diverse backgrounds as a mentee, a teaching assistant (TA), and a mentor. In my graduate school at University of Illinois (UIUC), Prof. Wilfred A. van der Donk, a meticulous patient educator, taught me critical thinking, project management, and soft skills needed as a researcher. I served as TA for two semesters at UIUC, as discussion session leaders for general chemistry, and as laboratory teacher for general chemistry laboratory classes. I engaged with my students, helping them to learn chemistry through discussing knowledge learnt in class and solving problem sets together, and helping them perform laboratory experiments using safe and professional operations. In the lab, I mentored a graduate student, Chunyu Wu. During my postdoctoral scholar period at the David Baker laboratory, I mentored four rotation students, and kept mentoring two of them after their joining the Baker lab. I provided guidance to my mentees from perspectives including teaching them computational and laboratory experiments, helping them plan experiments and manage projects. Many of my mentees developed a successful career in the bioengineering/biomedicine field. For example, the student I mentored during graduate school, Chunyu Wu, she successfully published research papers, got her Ph.D. degree and continued her career in the R & D section at a pharmaceutical company.

From my previous mentoring experiences, I learnt critical perspectives for setting mentees to success, and most importantly, guide them to develop their own enjoyable professional career. Teaching skill-wise, I learnt that the mentor needs to be patient, set clear goals, tailor teaching content to the level of the mentee, provide constant feedback, maintain the same requirements and standards, and help the mentee to be prepared for the mindset of a scientific career. Teaching style-wise, I learnt to not try to focus on ‘teaching students everything I know’, but to also guide them to resources they need, point them to different expertise resources, foster self-motivated learning, and most importantly, encourage them to work with and learn from each other. Teaching content-wise, I learnt that I can teach efficiently and effectively through teaching mentees the techniques on learning the fundamental knowledge through studying existing protocols and research papers, fostering critical thinkings to draw logical hypothesis, developing essential scientific soft skills including presenting, writing, and communicating, and making reasonable plans to perform effective experiments. Teaching-goal wise, I learnt I want to nurture the passion of my students, help them develop healthy mindsets towards scientific progress, help them discover their own interest, and guide them develop their own career path, and achieve their career goals.

Future Teaching Interests: I am comfortable on leading special topics in graduate and undergraduate courses, such as biochemistry, enzymology, and computational protein design, etc.

Based on my research interests, I would also love to develop a new curriculum in functional protein design, which focuses on using computational protein design methodologies to design functional proteins to solve problems in biology or biochemistry. This course, titled “Computational Functional Protein Design”, will be developed based on the Jupyter program developed at the Institute for Protein design (J. Chem. Educ. 2022, 99, 9, 3177) and aimed at teaching students to use well-developed computational tools to build proteins fulfilling functions of interests. The students will be learning as a cohort, discussing and teaching each other how to use python-based programs, brainstorming what type of functional proteins they would like to design to solve problems in the real world, and test the designed proteins in laboratories. I will provide examples to help the students to understand the functions of the computational tools. The course will introduce students to the state-of-the-art of protein design algorithms, introduce them into the field of protein engineering, and foster them with interests to use techniques from computer science to solve problems in biology, biochemistry, and chemistry fields. This course would be ideally a collaborative course with other departmental faculties to create guided brainstorming sessions for students for them to understand how to find interesting but practical problems to solve, and to test the designs the students created computationally.

Additionally, I am more than excited to design other courses with faculties from associated departments to create new courses, or add my input to existing courses.