RESUMO
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 0.473 and 0.124, respectively, for ß-turn types II 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.
