Non-coding RNAs (ncRNAs) are a growing class of transcripts, with lengths ranging from tens to several thousand of bases, involved in the regulation of a large number of biological processes and diseases. Many of these ncRNAs have emerged as the molecules of interest for prognostic, diagnostic, and therapeutic purposes in many diseases including cancer. Although ncRNAs do not encode proteins, they fold into complex structures to interact with target proteins, DNA, or other RNAs. In contrast to microRNAs (miRNAs) where researchers mainly focused on the nucleotide sequence for target prediction in the past, folding and structural conservation seems to be important to encode functions and interactions of long non-coding RNA (lncRNA). In this chapter, we discuss methods and tools available for the structural modeling of ncRNAs together with various examples from the literature where structural modeling helped decipher the function of ncRNAs. We also provide a step-by-step procedure to design 3D structures of ncRNAs combining state-of-the-art tools available toward the design of novel RNA therapeutics.
Keywords: Deep learning; Molecular docking; Molecular dynamic simulation; Non-coding RNAs; Structure modeling; miRNA-mRNA.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.