Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms. Author summary: AlphaFold2 is software that combines sequence similarity and structure templating with the power of Artificial Intelligence (AI) to bridge the connection between the primary protein structure (amino acid sequence) and higher-level 3D structure (secondary, tertiary, and quaternary). AlphaFold2's impressive and easily accessible nature makes it a bioinformatics tool that has been seeing a wide range of applications in biochemical research. Given this large-scale application, we examined whether Alphafold2 could be integrated into an undergraduate curriculum. We developed a novel module for a senior-level undergraduate biochemistry laboratory class. Our goal was to lay a solid foundation for other undergraduate instructors to be able to adapt this module to fit their classroom needs. While we implemented and ran all predictions on an internal university computing cluster, we recommend ColabFold for those instructors who do not have access to large-scale computational clusters or whose internal clusters cannot scale to their classroom sizes. We have outlined 3 metrics to be quantitatively investigated in the module to give both instructors and students metrics to evaluate model confidence. We have also included a template worksheet, lecture slides, and example scripts to enable instructors to rapidly develop a similar module. We hope that more departments and programs will integrate AlphaFold2 into their undergraduate curriculums, giving students a highly in-demand skill to prepare them for their transition into a career or graduate school. [ABSTRACT FROM AUTHOR]