Mathematical and computational models of RNA nanoclusters and their applications in data-driven environments

ABSTRACT

Ribonucleic acid (RNA) is a fundamental molecule having several favourable structural properties that can be used for various potential applications in the field of nanotechnology including biomedicine and bioengineering. Here in this review, we describe the computational and mathematical modellings of the RNA nanoclusters, such as the molecular dynamics simulation, coarse-grained modelling and continuum modelling. The RNA nanocubes, nanotubes and nanorings are some of the typical nanosized structures derived from RNA strands, and the details about such nanostructures have also been presented in this review. The RNA nanoprisms made out of the RNA building blocks via self-assembly of the RNA nanotriangles and their potential applications have been described. Furthermore, special attention is given to the earlier developed RNA nanoscaffolds from the RNA building blocks. We also present some recent results to describe the physical behaviour of the RNA nanotubes in different kinds of physiological solutions using molecular dynamics simulations. Finally, the recent applications of these computational models in several areas of medical sciences such as radiotherapy and drug delivery for cancer treatment and construction of RNA nanodevices have been highlighted. Several potential applications of artificial intelligence in this fast-growing field of RNA engineering have also been presented.

KEYWORDS:

• RNA nanoparticles
• nanotubes
• bionanotechnology
• computational modelling
• artificial intelligence based methods

Acknowledgments

Authors are grateful to the NSERC and the CRC Program for their support. This work was made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca) and Compute/Calcul Canada. The authors are grateful to Dr P. J. Douglas Roberts for helping with technical SHARCNET computational aspects. RM is also acknowledging the support of BERC 2018-2021 program, the Spanish Ministry of Science, Innovation and Universities through the BCAM Severo Ochoa accreditation SEV-2017-0718, and the Basque Government fund Artificial Intelligence in BCAM EXP. 2019/00432.

Disclosure statement

No potential conflict of interest was reported by the author(s).