3] Predicting protein secondary structure based on amino acid sequence

Abstract

Publisher Summary The prediction of protein secondary structure is a well-defined digital and linear problem. It is digital because the conformational state that each amino acid residue can adopt is assumed to be either a helix β strand or a coil so that correctly predicted residues are easily countable. It is linear because prediction is carried out to locate secondary-structure elements along the primary structure. This chapter presents the correspondence between two one-dimensional digital series of primary and secondary structures. Secondary-structure prediction is considered as one of the major obstacles in the process of elucidating protein structure. The reason for this difficulty lies in the fact that in protein folding, secondary structures are formed in a “context-dependent” manner. Thus, the prediction methods based on the local-sequence information are dominant. Prediction methods published recently, some of which are novel and others improvements in existing methods, are all of the local-sequence type. One of the new methods is the neural network approach, which is a computer learning algorithm developed for pattern recognition. Another new method is based on homology. In this method, weak and local sequence similarities with proteins of known structure are accumulated, and the majority of the secondary structure at corresponding residue sites is predicted. A different way to use sequence similarity data is to incorporate various sequence data homologous with a target sequence. Proteins belonging to the same evolutionary family share the same 3-D structure, and therefore the same secondary structure as well. Homologous sequence data available from a sequence database improves prediction accuracy.