Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network.
BACKGROUND
So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn.


RESULTS
A two-stage hybrid model developed to predict the beta-turn Types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of beta-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 beta-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 beta-turn sequence had an overall relationship with five beta-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 beta-turn Types I, II, IV and VIII. Our model also distinguished the different types of beta-turn in the embedded binary logit comparisons which have not carried out so far.


BACKGROUND
Available on request from the authors.
Asgary
Mehdi Poursheikhali
MP
Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.
Jahandideh
Samad
S
Abdolmaleki
Parviz
P
Kazemnejad
Anoshirvan
A
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D016015:Logistic Models; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D012044:Regression Analysis; D020539:Sequence Analysis, Protein / Q000379:methods*