%A Harinder Singh
%A Sandeep Singh
%A G.P.S. Raghava
%O Copyright of this article belongs to Wiley Online Library.
%J Proteins
%T In silico platform for predicting and initiating ?-turns in a protein at desired locations.
%X 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.
%N 5
%K analysis of beta turn residue; beta turn prediction; beta turn type prediction; designing of beta turn; statistical based beta turn prediction
%P 910-21
%V 83
%D 2015
%I Wiley
%L open1658