This paper presents a new computer method for folding RNA molecules that finds the conformation of minimum free energy using published stacking and destabilizing energies. The method is based on a dynamic programming algorithm and is more efficient, faster, and can fold larger molecules than existing biological methods. It is demonstrated by folding a 459-nucleotide immunoglobulin γ heavy chain mRNA fragment. The method is extended to incorporate additional information, such as chemical reactivity and enzyme susceptibility, as shown in the folding of two large fragments from the 16S ribosomal RNA of Escherichia coli. The method uses dynamic programming to compute the minimum free energy structure of an RNA sequence. It defines the energy of a structure based on the regions between bonds, not the bonds themselves. The algorithm computes the minimum free energy for all possible admissible structures formed from a subsequence of the RNA sequence. It considers different types of loops, such as hairpin, bulge, and interior loops, and calculates their energies accordingly. The algorithm is efficient, with a time complexity of O(N³), and can handle sequences up to 800 nucleotides long. The algorithm is implemented in Fortran on an IBM 3032 processor. It incorporates additional information, such as chemical modification and enzyme accessibility data, into the energy function to improve the accuracy of the predicted structure. The method is tested on the 459-nucleotide immunoglobulin γ heavy chain mRNA fragment, where it achieves a 15% improvement in minimum free energy compared to previous methods. It is also applied to the 16S ribosomal RNA of E. coli, where it produces a structure consistent with available experimental data. The results show that the algorithm can accurately predict RNA secondary structures, even when additional information is incorporated. The method is efficient and does not require human intervention, making it a powerful tool for RNA folding. However, the algorithm's performance is limited by available computer storage, which restricts it to sequences of up to 800 nucleotides. The method is further improved by incorporating phylogenetic data and other additional information to enhance the accuracy of the predicted structures. The algorithm is a valuable tool for studying the secondary structure of RNA molecules and has the potential to be applied to a wide range of RNA sequences.This paper presents a new computer method for folding RNA molecules that finds the conformation of minimum free energy using published stacking and destabilizing energies. The method is based on a dynamic programming algorithm and is more efficient, faster, and can fold larger molecules than existing biological methods. It is demonstrated by folding a 459-nucleotide immunoglobulin γ heavy chain mRNA fragment. The method is extended to incorporate additional information, such as chemical reactivity and enzyme susceptibility, as shown in the folding of two large fragments from the 16S ribosomal RNA of Escherichia coli. The method uses dynamic programming to compute the minimum free energy structure of an RNA sequence. It defines the energy of a structure based on the regions between bonds, not the bonds themselves. The algorithm computes the minimum free energy for all possible admissible structures formed from a subsequence of the RNA sequence. It considers different types of loops, such as hairpin, bulge, and interior loops, and calculates their energies accordingly. The algorithm is efficient, with a time complexity of O(N³), and can handle sequences up to 800 nucleotides long. The algorithm is implemented in Fortran on an IBM 3032 processor. It incorporates additional information, such as chemical modification and enzyme accessibility data, into the energy function to improve the accuracy of the predicted structure. The method is tested on the 459-nucleotide immunoglobulin γ heavy chain mRNA fragment, where it achieves a 15% improvement in minimum free energy compared to previous methods. It is also applied to the 16S ribosomal RNA of E. coli, where it produces a structure consistent with available experimental data. The results show that the algorithm can accurately predict RNA secondary structures, even when additional information is incorporated. The method is efficient and does not require human intervention, making it a powerful tool for RNA folding. However, the algorithm's performance is limited by available computer storage, which restricts it to sequences of up to 800 nucleotides. The method is further improved by incorporating phylogenetic data and other additional information to enhance the accuracy of the predicted structures. The algorithm is a valuable tool for studying the secondary structure of RNA molecules and has the potential to be applied to a wide range of RNA sequences.