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Wireframe DNA nanocages, an important type of DNA nanomaterials, exhibit exceptional programmability for chemical modifications, along with tunable size and shape. Nevertheless, the impact of their conformational fluctuations on cage design has not been thoroughly explored, despite speculation regarding its influence on biomedical applications. This study marks the first systematic examination of the conformational dynamics of prismatic DNA nanocages through molecular modeling and simulation. By comparing four different DNA nanocage topologies, we uncover design parameter combinations and conditions that facilitate access to varying conformational states. We observe the expansion and contraction of these cages across various topologies, hybridization states, and ionic environments (Mg2+/Na+), with their volumes varying from 15% to 150% of the ideal cage volumes. Our results indicate that the dynamics of DNA cages is influenced by the concentrations of Mg2+ and Na+ ions. Additionally, the flexibility of specific DNA strands can be manipulated, thereby altering the cage volume, through the selective hybridization of the cage edges. Ultimately, the conformational dynamics of DNA nanocages are captured in atomic detail. This study offers valuable modeling tools and methodologies to assist future DNA nanocage design endeavors. 

ABSTRACT Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.