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

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.