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Proteins. 2009 Feb 1;74(2):344-52. doi: 10.1002/prot.22164.

Prediction of turn types in protein structure by machine-learning classifiers.

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Johann Wolfgang Goethe-Universität, Institut für Organische Chemie & Chemische Biologie, Siesmayerstr. 70, D-60323 Frankfurt am Main, Germany.


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 approximately 0.6 and a prediction accuracy of 80%. Probabilistic neural networks were developed for beta-turn type prediction. The method was able to distinguish between five types of beta-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.

[Indexed for MEDLINE]

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