Programs and computational frameworks for predicting RNA sequences with desired folding properties are continually being developed and expanded. A decade has passed since they were last reviewed in this journal, and this brief review provides an update to the review published at that time. Given a target secondary structure, these programs aim to predict RNA sequences that fold into the desired structure while satisfying various constraints. This procedure is known as inverse RNA folding. Traditionally, inverse RNA folding has been used to design optimized RNAs with favorable properties. This updated review covers some of the most widely used freeware programs developed for this purpose over the past decade. RNAinverse, part of the Vienna RNA package, was the first program devised to address the inverse RNA folding problem, and many subsequent programs were described in the earlier review. Some of the most important computational frameworks are the Infrared framework and DesiRNA. In addition, RNA design capabilities have been incorporated into the RNAstructure package, while NUPACK, as well as MoiRNAiFold, MODENA, incaRNAfbinv, and related tools have undergone recent updates. A variety of strategies have also emerged to address the problem of 3D RNA design and RNA-RNA interactions. The various programs mentioned employ distinct approaches, ranging from replica exchange Monte Carlo to constraint satisfaction, as well as Boltzmann sampling and machine learning approaches. Machine learning methods are being developed for emerging applications in biotechnology such as messenger RNA(mRNA) design and CRISPR guide RNA (gRNA) design. This brief review examines these programs and provides a timely update.
Mukherjee et al. (Fri,) studied this question.