<Dd> a term with similar meaning to a structural motif . Tertiary structure is the three - dimensional or globular structure formed by the packing together or folding of secondary structures of a polypeptide chain . </Dd> <P> Secondary structure prediction is a set of techniques in bioinformatics that aim to predict the local secondary structures of proteins based only on knowledge of their amino acid sequence . For proteins, a prediction consists of assigning regions of the amino acid sequence as likely alpha helices, 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 (or similar e.g. STRIDE) applied to the crystal structure of the protein . Specialized algorithms have been developed for the detection of specific well - defined patterns such as transmembrane helices and coiled coils in proteins . </P> <P> The best modern methods of secondary structure prediction in proteins reach about 80% accuracy; this high accuracy allows the use of the predictions as feature improving fold recognition and ab initio protein structure prediction, classification of structural motifs, and refinement of sequence alignments . The accuracy of current protein secondary structure prediction methods is assessed in weekly benchmarks such as LiveBench and EVA . </P> <P> Early methods of secondary structure prediction, introduced in the 1960s and early 1970s, focused on identifying likely alpha helices and were based mainly on helix - coil transition models . Significantly more accurate predictions that included beta sheets were introduced in the 1970s and relied on statistical assessments based on probability parameters derived from known solved structures . These methods, applied to a single sequence, are typically at most about 60 - 65% accurate, and often underpredict beta sheets . The evolutionary conservation of secondary structures can be exploited by simultaneously assessing many homologous sequences in a multiple sequence alignment, by calculating the net secondary structure propensity of an aligned column of amino acids . In concert with larger databases of known protein structures and modern machine learning methods such as neural nets and support vector machines, these methods can achieve up to 80% overall accuracy in globular proteins . The theoretical upper limit of accuracy is around 90%, partly due to idiosyncrasies in DSSP assignment near the ends of secondary structures, where local conformations vary under native conditions but may be forced to assume a single conformation in crystals due to packing constraints . Limitations are also imposed by secondary structure prediction's inability to account for tertiary structure; for example, a sequence predicted as a likely helix may still be able to adopt a beta - strand conformation if it is located within a beta - sheet region of the protein and its side chains pack well with their neighbors . Dramatic conformational changes related to the protein's function or environment can also alter local secondary structure . </P>

Which of the following is the best representation of a protein chain