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Bioinformatics Advance Access published online on June 28, 2007
Bioinformatics, 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 prediction of ß-turn types using multinomial logistic regression and artificial neural network

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

1Department of biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran
2Department of biostatistics, Faculty of Medical Sciences, Tarbiat Modares university, Tehran, Iran

*To whom correspondence should be addressed. Dr. Parviz Abdolmaleki, E-mail: parviz{at}modares.ac.ir


  Abstract

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

Results: A two-stage hybrid model developed to predict the ß-turn types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of ß-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 ß-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 ß-turn sequence had an overall relationship with 5 ß-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 nine 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 ß-turn types I, II, IV and VIII which are best among previously reported results. Our model also distinguished the different types of ß-turn in the embedded binary logit comparisons which have not carried out so far.

Availability: Available on request from the authors.

Associate Editor: Prof. Anna Tramontano

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

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