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Bioinformatics Advance Access originally published online on June 28, 2007
Bioinformatics 2007 23(23):3125-3130; doi:10.1093/bioinformatics/btm324
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Analysis and identification of β-turn types using multinomial logistic regression and artificial neural network

Mehdi Poursheikhali Asgary 1, Samad Jahandideh 1, Parviz Abdolmaleki 1,* and Anoshirvan Kazemnejad 2

1Department of Biophysics, Faculty of Basic Sciences and 2Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

*To whom correspondence should be addressed.


  Abstract

Motivation: So far various statistical and machine learning techniques applied for prediction of β-turns. The majority of these techniques have been only focused on the prediction of β-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of β-turn.

Results: A two-stage hybrid model developed to predict the β-turn Types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of β-turn types using a self-consistency test procedure. The extracted parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in β-turn sequence. The most significant parameters were then selected using multinomial logistic regression model. Among these, the occurrences of glutamine, histidine, glutamic acid and arginine, respectively, in positions i, i + 1, i + 2 and i + 3 of β-turn sequence had an overall relationship with five β-turn types. A neural network model was then constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains by 9-fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.235, 0.473, 0.103 and 0.124, respectively, for β-turn Types I, II, IV and VIII. Our model also distinguished the different types of β-turn in the embedded binary logit comparisons which have not carried out so far.

Availability: Available on request from the authors.

Contact: parviz{at}modares.ac.ir

Associate Editor: Anna Tramontano

Received on February 25, 2007; revised on May 20, 2007; accepted on June 12, 2007

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