creators_name: Singh, Harinder
creators_name: Singh, Sandeep
creators_name: Raghava, G.P.S.
type: article
datestamp: 2015-07-11 05:32:16
lastmod: 2015-07-11 05:32:16
metadata_visibility: show
title: In silico platform for predicting and initiating β-turns in a protein at desired locations.
ispublished: pub
subjects: QR
keywords: analysis of beta turn residue; beta turn prediction; beta turn type prediction; designing of beta turn; statistical based beta turn prediction
note: Copyright of this article belongs to Wiley Online Library.
abstract: 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.
date: 2015-05
date_type: published
publication: Proteins
volume: 83
number: 5
publisher: Wiley
pagerange: 910-21
refereed: TRUE
issn: 1097-0134
official_url: http://onlinelibrary.wiley.com/doi/10.1002/prot.24783/epdf
citation:   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