Abstract A global strategy for estimating the entropy of long sequences of RNA is proposed to help improve the predictive capacity of RNA secondary structure dynamic programming algorithm (DPA) free energy (FE) minimization methods. These DPA strategies only consider the effects that occur in the immediate (nearest neighbor) vicinity of a given base pair (bp) in a secondary structure plot. They are therefore defined as nearest-neighbor secondary structure (NNSS) strategies. The new approach utilizes the statistical properties of the Gaussian polymer chain model to introduce both local and global contributions to the entropy of a given secondary structure. These entropic contributions are primarily a function of the persistence length of the RNA. Limits on the domain size are strongly suggested by this model and these limits are a function of both the length and the percentage of bp enclosed within a given domain. The model generalizes the penalties found in the NNSS algorithms. The approach considers the importance of flexibility in the folding and stability of RNA by considering the role of the persistence length in a biopolymer structure. The theory also suggests that molecular machinery may also take advantage of this global entropic effect to bring about catalytic effects. The applications can also be extended to protein structure calculations with some additional considerations.