Computational Methods for Protein Secondary Structure Prediction Using Multiple Sequence Alignments

Jaap      Heringa

doi:10.2174/1389203003381324

Abstract

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago (1-3). Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible (e.g., Ref 4). This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced (5).

Keywords: Computational methods, Multiple Sequence alignments, 3D modeling, Globular proteins, Fold recognition, Protein folding, Single sequence prediction, PHD, NNSSP