	Secondary structure prediction involves a set of methods in bioinformatics that aim to predict the local secondary structures of proteins and RNA sequences based only on knowledge of their primary structure – amino acid or nucleotide sequence, respectively. For proteins, a prediction consists of assigning regions of the amino acid sequence as highly probable alpha helixes, beta strands (often noted as "extended" conformations), or turns. The success of a prediction is determined by comparing it to the results of the DSSP algorithm applied to the crystal structure of the protein; for nucleic acids, it may be determined from the hydrogen bonding pattern. Specialized algorithms have been developed to detect specific well-defined patterns such as transmembrane helixes and coiled coils in proteins, or canonical micro-RNA structures in RNA.