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Grace Hsu and Andrew Catalano (Smart Biology Inc., Ottawa, ON, Canada)
Scientific visualization plays a crucial role in conveying complex molecular processes to students and researchers, yet educational materials often oversimplify molecular environments, losing key structural and mechanistic details. As molecular animation has matured as a discipline, the wealth of data from the Protein Data Bank (PDB), combined with AI-driven predictions from AlphaFold, allows us to fill in missing structural information with greater accuracy. To illustrate this approach, we present the construction of a model of the Mre11-Rad50-Nbs1 (MRN) complex, a key player in double-stranded DNA repair. By integrating PDB structures and AlphaFold predictions, we refined our model to balance structural accuracy with conceptual clarity. Through careful selection of structural data and thoughtful levels of abstraction, we ensure that our animation remains faithful to experimental findings while communicating the MRN complex’s role in double-strandedDNA repair to students.