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Prediction and classification of α‐turn types
Author(s) -
Chou KouChen
Publication year - 1997
Publication title -
biopolymers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/(sici)1097-0282(199712)42:7<837::aid-bip9>3.0.co;2-u
Subject(s) - pentapeptide repeat , alpha (finance) , jackknife resampling , sequence (biology) , globular protein , turn (biochemistry) , markov chain , chemistry , beta (programming language) , computational biology , artificial intelligence , computer science , machine learning , mathematics , statistics , biology , crystallography , estimator , peptide , biochemistry , construct validity , programming language , psychometrics
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