Learning computational approaches to unravel enzyme catalysis

The FEBS Advanced Course Computational Approaches to Understanding and Engineering Enzyme Catalysis (Zagreb, 2023) offered a range of activities for PhD students and young postdocs to learn about using in silico tools in their work. On this post, a PhD student describes her experience of the course.
Learning computational approaches to unravel enzyme catalysis

The FEBS Advanced course on Computational Approaches to Understanding and Engineering Enzyme Catalysis took place from the 25th to the 29th of September 2023 in Zagreb, Croatia, at the Faculty of Science of the University of Zagreb. The course was designed as a stepping stone for enzyme experimentalists to explore computational approaches to studying enzyme catalysis.

As a PhD student, I was highly interested in the topic of the course, as my research focuses on enzymes. Enzymes are fascinating biologically relevant protein macromolecules that catalyze reactions (i.e., enzyme catalysis). While I've been fascinated with enzymes for quite some time, my experience has been primarily in wet-lab work involving overexpressing and characterizing enzymes. With limited experience in in silico protein design, I decided to apply for the course, as it could significantly benefit my current and future projects related to enzyme technology and similar fields.

The course was well-organized, and the days were filled with interesting lectures and mostly complemented with computational tutorials in the afternoons. The topics covered during the course included molecular dynamics, computational-aided engineering of enzymes, visualization of enzymes, enzyme kinetics, and applications of enzymes. As a young scientist, I find tutorials to be the most interactive part of such courses, as they provide opportunities for personal interactions with the tutor and other attendees. I was eagerly looking forward to attending the tutorials.

One of the most interesting tutorials for me was about the EMBL-EBI data resources and tools, specifically the Worldwide Protein Data Bank (wwPDB), presented by Dr. Deepti Gupta, a Senior Biocurator and Project Manager. During the 2-hour tutorial, we learned about this extensive repository of protein structures and how it can assist us in answering everyday questions and enhancing our understanding of specific proteins (enzymes) and their substrates, as well as interactions with other macromolecules. The most intriguing discovery for me was Mol* (MolStar), a web-based open-source toolkit for visualizing and analyzing large-scale molecular data. As a protein scientist, I believe that effectively visualizing protein molecules while highlighting their most important features/domains is a challenging task. Additionally, considering that visualizations of structures and structural predictions should also be aesthetically pleasing and easy to comprehend in terms of colors, I believe this tool is ideal for such purposes. If you're interested in using it, please visit the documentation, which can help you get started. I certainly plan to use it in my daily work, as well as for posters and publications.

Lastly, during the course, I had the opportunity to meet many young scientists who are also studying enzymes. Networking and making new friends from different countries are important aspects of attending such courses. Not only did I meet my peers and exchange ideas, but I also established connections that may prove beneficial for future scientific collaborations.

I encourage everyone to check the FEBS website for information on future events and consider attending lectures or courses.

Top image by Anja Kostelac. Picture description: Bacterial pyranose oxidase from Streptomyces canus, structural prediction by RoseTTAFold visualized by Mol*.

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