Abstract: 　This course holds a significant position in the comprehensive professional talent training system. This experiment relies on a high-performance computing platform, incorporating AlphaFold2 and Schrdinger software into the experimental teaching of protein science and engineering. By utilizing bioinformatics methods such as protein structure prediction, molecular docking, and molecular dynamics simulation, students can gain deeper insights into the catalytic mechanisms of proteins. The teaching experiment employs a differentiated instruction approach and incorporates assessment methods based on individual performance and group presentations. This design aims to enhance students’ abilities in self-directed learning, teamwork, information retrieval, tool application, problem analysis, and knowledge exploration. By analyzing students’ evaluations of teaching, we can further improve teaching content and enhance teaching effectiveness. This course serves as a valuable reference for the innovation of teaching experiments in similar courses such as enzyme engineering. It not only provides students with the opportunity to learn and use advanced scientific research tools but also plays a significant role in enhancing learning interest, improving teaching effectiveness, and cultivating innovative talents with interdisciplinary backgrounds.

Key words: protein science and engineering, experiment teaching, protein structure, docking, molecular dynamics