This paper presents a new computer method for folding RNA molecules to find the conformation of minimum free energy, utilizing published values of stacking and destabilizing energies. The method is based on a dynamic programming algorithm from applied mathematics, which is more efficient and faster than previous methods in the biological literature. The authors demonstrate the power of their method by folding a 459-nucleotide immunoglobulin γ heavy chain messenger RNA fragment, achieving a 15% improvement in free energy over previous results. They also show how to incorporate additional information, such as chemical reactivity and enzyme susceptibility, into the algorithm. This is illustrated by folding two large fragments from the 16S ribosomal RNA of Escherichia coli. The paper discusses the limitations and potential improvements of the method, emphasizing the need for more sophisticated thermodynamic rules and the integration of phylogenetic data.This paper presents a new computer method for folding RNA molecules to find the conformation of minimum free energy, utilizing published values of stacking and destabilizing energies. The method is based on a dynamic programming algorithm from applied mathematics, which is more efficient and faster than previous methods in the biological literature. The authors demonstrate the power of their method by folding a 459-nucleotide immunoglobulin γ heavy chain messenger RNA fragment, achieving a 15% improvement in free energy over previous results. They also show how to incorporate additional information, such as chemical reactivity and enzyme susceptibility, into the algorithm. This is illustrated by folding two large fragments from the 16S ribosomal RNA of Escherichia coli. The paper discusses the limitations and potential improvements of the method, emphasizing the need for more sophisticated thermodynamic rules and the integration of phylogenetic data.