A NOVEL HYBRID METHOD OF B-TURN IDENTIFICATION IN PROTEIN USING BINARY LOGISTIC REGRESSION AND NEURAL NETWORK

From both the structural and functional points of view, beta-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify beta-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of beta-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a beta-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Q(total)) of 74, which is comparable with results of the other beta-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying beta-turns in proteins.