Prediction of turn types in protein structure by machine-learning classifiers |
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Authors: | Meissner Michael Koch Oliver Klebe Gerhard Schneider Gisbert |
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Institution: | 1. Johann Wolfgang Goethe‐Universit?t, Institut für Organische Chemie & Chemische Biologie, Siesmayerstr. 70, D‐60323 Frankfurt am Main, Germany;2. Philipps‐Universit?t, Institut für Pharmazeutische Chemie, Marbacher Weg 6, D‐35032 Marburg, Germany |
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Abstract: | 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. |
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Keywords: | bioinformatics kernel function prediction probabilistic neural network secondary structure self‐organizing map support vector machine turn classification |
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