title: In silico platform for predicting and initiating β-turns in a protein at desired locations. creator: Singh, Harinder creator: Singh, Sandeep creator: Raghava, G.P.S. subject: QR Microbiology description: Numerous studies have been performed for analysis and prediction of β-turns in a protein. This study focuses on analyzing, predicting, and designing of β-turns to understand the preference of amino acids in β-turn formation. We analyzed around 20,000 PDB chains to understand the preference of residues or pair of residues at different positions in β-turns. Based on the results, a propensity-based method has been developed for predicting β-turns with an accuracy of 82%. We introduced a new approach entitled "Turn level prediction method," which predicts the complete β-turn rather than focusing on the residues in a β-turn. Finally, we developed BetaTPred3, a Random forest based method for predicting β-turns by utilizing various features of four residues present in β-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 β-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 β-turns and their types in proteins. This web server also predicts minimum number of mutations required to initiate or break a β-turn in a protein at specified location of a protein. publisher: Wiley date: 2015-05 type: Article type: PeerReviewed relation: http://onlinelibrary.wiley.com/doi/10.1002/prot.24783/epdf identifier: Singh, Harinder and Singh, Sandeep and Raghava, G.P.S. (2015) In silico platform for predicting and initiating β-turns in a protein at desired locations. Proteins, 83 (5). pp. 910-21. ISSN 1097-0134 relation: http://crdd.osdd.net/open/1658/