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Prediction of turn types in protein structure by machine‐learning classifiers
Author(s) -
Meissner Michael,
Koch Oliver,
Klebe Gerhard,
Schneider Gisbert
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22164
Subject(s) - turn (biochemistry) , artificial intelligence , machine learning , computer science , physics , nuclear magnetic resonance
We present machine learning approaches for turn prediction from the amino acid sequence. Different turn classes and types were considered based on a novel turn classification scheme. We trained an unsupervised (self‐organizing map) and two kernel‐based classifiers, namely the support vector machine and a probabilistic neural network. Turn versus non‐turn classification was carried out for turn families containing intramolecular hydrogen bonds and three to six residues. Support vector machine classifiers yielded a Matthews correlation coefficient (mcc) of ∼0.6 and a prediction accuracy of 80%. Probabilistic neural networks were developed for β‐turn type prediction. The method was able to distinguish between five types of β‐turns yielding mcc > 0.5 and at least 80% overall accuracy. We conclude that the proposed new turn classification is distinct and well‐defined, and machine learning classifiers are suited for sequence‐based turn prediction. Their potential for sequence‐based prediction of turn structures is discussed. Proteins 2009. © 2008 Wiley‐Liss, Inc.