# DNA Nanodevices as Mechanical Probes of Protein Structure and Function
## 5 Future Enabling Technologies for Protein-DNA Nanodevices
### Computational Simulation of Protein-DNA Nanostructures  
The ability to simulate hybrid protein-DNA nanostructures to atomic accuracy would enable great advances in the design of these systems and facilitate future applications in the areas discussed in Sections 2-4. Such capability would also enable the design of hybrid nanostructures comprised of both building blocks, [6] as well as help interpret experimental data (e.g., protein deformation using DNA nanodevices or interpretation of cryo-EM maps for proteins scaffolded on origami). Protein design software, such as Rosetta [63], has enabled remarkable advances in the design of complex protein nanostructures and devices. A similar software package for protein-DNA nanotechnology, however, is lacking. Below we discuss DNA nanostructure modeling and outline the challenges, and some recent advances, in merging these methods with protein simulation. Since the invention of DNA origami, various computational design tools have been used to obtain increasingly complex nanostructures. The most commonly used tools include Cadnano [64] for 2D and 3D origami designs, and Athena [65] for 3D wireframe designs. However, these design tools have been, for the most part, limited to constructs comprised of DNA only. Currently, only the ADENITA [66] and oxView tools [67] (Figure 7) allow for representing a protein along with a designed DNA nanostructure. The inclusion of proteins (typically based on a known structure from the PDB database) in the 3D representation of the design tool is crucial for determining the optimal placement of linkers for rigid positioning of the proteins, which in turn is also critical for spacing proteins appropriately or determining the forces exerted upon them by a DNA nanodevice. The modeling tools that can incorporate proteins in this way have, however, been introduced only recently and are currently still missing an accurate representation for different types of linkers. Besides using computer-aided design, molecular simulation has also become an important part of the DNA structure characterization pipeline [68]. Simulations can be used to optimize different designs _in silico_, explain and rationalize behavior observed in experiments and probe nanostructures at a level of detail not accessible experimentally. The most commonly used models to simulate molecules in a biological context are atomic-resolution models, where each particle corresponds to a single atom. Programs such as AMBER, GROMACS, [69] or NAMD [70] can be used to study the molecules and their interactions, relying on parallel computations on multiple CPUs and/or GPUs. The parametrization of interactions between atoms is called a force-field. Two of the  
Figure 7: A DNA-protein nanocage as represented in the ANM-oxDNA model using the oxView editing tool. Reprinted with permission from ref. [67], Copyright 2020 Oxford University Press. most popular ones for protein simulation are based on CHARMM and AMBER-based force fields [71]. Recently, there has been significant effort to also develop force-fields that better represent DNA [72] and RNA [73]. However, probing the interactions _between_ proteins and nucleic acids remains a challenging topic in terms of parametrization of the force-field in order to reproduce interaction free-energies more accurately or predict protein-DNA interactions. Besides the difficulties mentioned above, fully atomistic models are also limited in the timescales that can be studied. With the current latest hardware, up to tens of microseconds can be simulated, requiring up to several months of simulation on multicore systems. DNA nanostructures like origami are typically made up of tens of thousands of nucleotides, and include events on timescales that can reach several hours, making the systems extremely challenging targets for simulation. So far, fully atomistic studies of DNA nanostructures have been generally limited to conformational sampling up to fractions of a microsecond in length [74].