PKProbDesign: RNA inverse folding including pseudoknots by optimizing thermodynamic folding probability

MotivationRNA inverse folding, the design of RNA sequences that fold into specified target structures, is a central problem in RNA design, with applications in functional RNA engineering, synthetic biology, and nucleic-acid therapeutics. This task becomes especially challenging for pseudoknotted target structures because pseudoknots disrupt the nested structure assumed by standard thermodynamic folding models. Existing pseudoknot inverse-folding methods often rely on structure-predictor-based objectives. Direct optimization of the thermodynamic folding probability of a specified pseudoknotted target remains limited. This requires an evaluator that can assign target-specific folding probabilities within a pseudoknot-aware ensemble and can be used as an optimization signal. ResultsWe present PKProbDesign, a sampling-based inverse-folding framework that directly optimizes a thermodynamic folding-probability objective for pseudoknotted targets. For each target, candidate sequences are scored by combining the folding probability of a pseudoknot-free scaffold with the conditional folding probability of the remaining extension component. On 354 PseudoBase++ targets, PKProbDesign achieved the highest folding probability on 221 targets, compared with 117 for DesiRNA and 16 for MODENA. ConclusionsPKProbDesign demonstrates that pseudoknot inverse folding can be formulated around target folding probabilities rather than structure-prediction agreement alone. By combining scaffold decomposition with HFold/CParty-consistent conditional-ensemble evaluation, the method provides a practical probability-based framework for designing sequences for density-2 pseudoknotted targets. AvailabilityThe source code of PKProbDesign is available at https://github.com/TakumiOtagaki/PKProbDesign.