RETRACTED: Analysis and prediction of β-turn types using multinomial logistic regression and artificial neural network.
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.



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 5 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 nine fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.473 and 0.124, respectively, for β-turn types II and VIII which are best among previously reported results. Our model also distinguished the different types of β-turn in the embedded binary logit comparisons which have not carried out so far.



Available on request from the authors.
Mehdi
Poursheikhali Asgary
PA
Department of biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.
Parviz
Abdolmaleki
A
Department of biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.
Anoshirvan
Kazemnejad
K
Department of biostatistics, Faculty of Medical Sciences, Tarbiat Modares university, Tehran, Iran.
Samad
Jahandidehs
J
Department of biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.