In silico platform for predicting and initiating β-turns in a protein at desired locations

Numerous studies have been performed for analysis and prediction of -turns in a protein. This study focuses on analyzing, predicting, and designing of -turns to understand the preference of amino acids in -turn formation. We analyzed around 20,000 PDB chains to understand the preference of residues or pair of residues at different positions in -turns. Based on the results, a propensity-based method has been developed for predicting -turns with an accuracy of 82%. We introduced a new approach entitled Turn level prediction method, which predicts the complete -turn rather than focusing on the residues in a -turn. Finally, we developed BetaTPred3, a Random forest based method for predicting -turns by utilizing various features of four residues present in -turns. The BetaTPred3 achieved an accuracy of 79% with 0.51 MCC that is comparable or better than existing methods on BT426 dataset. Additionally, models were developed to predict -turn types with better performance than other methods available in the literature. In order to improve the quality of prediction of turns, we developed prediction models on a large and latest dataset of 6376 nonredundant protein chains. Based on this study, a web server has been developed for prediction of -turns and their types in proteins. This web server also predicts minimum number of mutations required to initiate or break a -turn in a protein at specified location of a protein. Proteins 2015; 83:910-921. (c) 2015 Wiley Periodicals, Inc.