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

Proteins
Harinder SinghGajendra P S Raghava

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 f...Continue Reading

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Citations

Sep 16, 2016·Scientific Reports·Alexandre G de Brevern
Jan 29, 2019·Database : the Journal of Biological Databases and Curation·Piyush AgrawalGajendra P S Raghava
Nov 18, 2020·Briefings in Bioinformatics·Neelam SharmaGajendra P S Raghava
Jul 17, 2020·ACS Infectious Diseases·Yanbao YuKaren E Nelson

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