Computational selection of allosteric RNAs: A review

Ligand-responsive ribozymes can be generated either through in vitro selection or through algorithm-driven computational design approaches that explicitly model RNA secondary structure and energetics. While in vitro selection relies on experimental screening and enrichment, Computational design strategies employ random search and evolutionary algorithms, including partition function-based approaches that predict RNA folding according to thermodynamic principles. These approaches enable quantitative evaluation of folding probabilities, free-energy states, and ligand-dependent conformational switching. By programming structural and thermodynamic limitations, allosteric ribozymes can be systematically engineered to implement Boolean logic-gate behavior, in which catalytic activity serves as a binary output. Compared with in vitro selection, in silico design offers significant advantages in speed, scalability, and reproducibility. Algorithmic workflows can be fully automated and executed by standalone computer programs that generate, evaluate, and filter large numbers of candidate sequences. This automation enables rapid exploration of the design, supports high-throughput generation of programmable allosteric ribozymes, highlighting the central role of computational modeling and algorithmic control in modern RNA engineering.