gamma-Turn types prediction in proteins using the support vector machines.
Recently, two different models have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2002. An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508-1514; 2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods is inability in predicting gamma-turns types. Thus, there is a need to predict gamma-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting gamma-turn types in proteins. The high rates of prediction accuracy showed that the formation of gamma-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.
Jahandideh
Samad
S
Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box 14115/175, Tehran, Iran.
Sarvestani
Amir Sabet
AS
Abdolmaleki
Parviz
P
Jahandideh
Mina
M
Barfeie
Mahdyar
M
D000465:Algorithms; D000626:Aminopeptidases / Q000737:chemistry; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008956:Models, Chemical; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins; D000626:Aminopeptidases; C023191:PepT tripeptidase