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Computational methods can greatly aid nucleic acid fluorescence experiments by either offering fully detailed atomic insights into the conformations and interactions present in the studied system or by providing accurate simulations of the fundamental parameters. Fluorescence-based optical biosensors show great potential for clinical diagnosis of life-altering diseases with a very high specificity. Many of the designs for such rely on the concept of Förster resonance energy transfer (FRET). Currently, the methods used experimentally make use of theoretical assumptions which fundamentally affect the results. Having a detailed atomistic overview or significant simulated parameters could improve the understanding of the calculations and provide much more accurate outcomes. However, there are many challenges that need to be addressed before standardized computational protocols can be employed. This review is meant to highlight the progress made for computational methods used to simulate FRET experiments for nucleic acid probes. Recent advances have been made in computational tools, such as force field parametrizations and improved protocols. Complementary simulations to experimental data are also comprised in the this review.

GOPY is a free and open-source Python tool specifically written to automate the generation of 2D graphene-based molecular models such as pristine graphene (PG) and several graphene derivatives i.e. graphene oxide (GO), reduced graphene oxide (rGO), aminated polyethylene glycol functionalised reduced graphene oxide (rGO-PEG-NH2), and N-doped graphene (NG) in the Protein Data Bank file format (PDB). These models are generally built manually, but the process can become lengthy and cumbersome. That is especially the case when investigating larger molecules such as those used in Molecular Dynamics (MD) simulations. Using GOPY significantly speeds up the process from hours to minutes, reducing potential bias that may come with the manual placement of functional groups on a graphene layer. Moreover, the building procedure becomes effortless for the researcher, granting the possibility of producing larger and more complex molecular models than one would be able to build manually. Of its more intensive tasks, the generation of a 4 x 4 nm2rGO-PEG-NH2layer takes about 9 min on a CodeOcean capsule. Each model is generated in the PDB format, which is easily convertible to a wide array of other molecular formats.