All existing algorithms for predicting the content of protein secondary structure elements have been based on the conventional amino-acid-composition, where no sequence coupling effects are taken into account. In this article, an algorithm was developed for predicting the content of protein secondary structure elements that was based on a new amino-acid-composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent dataset test. Both indicated a remarkable improvement obtained when using the current algorithm to predict the contents of alpha-helix, beta-sheet, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend and random coil. Examples of the improved accuracy by introducing the new amino-acid-composition, as well as its impact on the study of protein structural class and biologically function, are discussed.

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Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, β‐turns and γ‐turns have been extensively studied, but α‐turns were little investigated. Recently, a systematic search for α‐turns was conducted by V. Pavone et al. [(1996) Biopolymers, Vol. 38, pp. 705–721] from 190 proteins (221 protein chains). They found 356 α‐turns that were classified into nine different types according to their backbone trajectory features. In view of this new discovery, a sequence‐coupled model based on Markov chain theory is proposed for predicting the α‐turn types in proteins. The high rates of correct prediction by resubstitution test and jackknife test imply that that the formation of different α‐turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the role of its interaction with the other part of a protein cannot be completely ignored. The algorithm presented here can also be used to conduct the prediction in which a distinction between α‐turns and non‐α‐turns is also required. © 1997 John Wiley & Sons, Inc. Biopoly 42: 837–853, 1997

A 1–4 and 2–3 residue‐correlation model is proposed to predict the β‐turns in proteins. The average rate of correct prediction for the 455 β‐turn tetrapeptides and 4018 non‐β‐turn tetrapeptides in the training data base is 80.1%, and that for the 223 β‐turn tetrapeptides and 12562 non‐β‐turn tetrapeptides in the testing data base is 80.9%. Compared with the rates of correct prediction based on the residue‐independent model reported previously, the quality of prediction is significantly improved by the new model, implying that the correlation effect between the 1st and the 4th residues and that between the 2nd and 3rd residues along a tetrapeptide are important for forming a β‐turn in a protein during the process of its folding. © 1997 John Wiley & Sons, Inc. Biopoly 41: 673–702, 1997

Can the coupling effect among different amino acid components be used to improve the prediction of protein structural classes? The answer is yes according to the study by Chou and Zhang (Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995), but a completely opposite conclusion was drawn by Eisenhaber et al. when using a different dataset constructed by themselves (Proteins 25:169-179, 1996). To resolve such a perplexing problem, predictions were performed by various approaches for the datasets from an objective database, the SCOP database (Murzin, Brenner, Hubbard, and Chothia. J. Mol. Biol. 247:536-540, 1995). According to SCOP, the classification of structural classes for protein domains is based on the evolutionary relationship and on the principles that govern the 3D structure of proteins, and hence is more natural and reliable. The results from both resubstitution tests and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporated with the coupling effect among different amino acid components are significantly higher than those by the algorithms without using such an effect. It is elucidated through an analysis that the main reasons for Eisenhaber et al. to have reached an opposite conclusion are the result of (1) misusing the component-coupled algorithm, and (2) using a conceptually incorrect rule to classify protein structural classes. The formulation and analysis presented in this article are conducive to clarify these problems, helping correctly to apply the prediction algorithm and interpret the results.