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 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.
Meissner
Michael
M
Johann Wolfgang Goethe-Universität, Institut für Organische Chemie & Chemische Biologie, Siesmayerstr. 70, D-60323 Frankfurt am Main, Germany.
Koch
Oliver
O
Klebe
Gerhard
G
Schneider
Gisbert
G
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins