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 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.
Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.
Harinder
H
Singh
Sandeep
S
Singh
Gajendra P S
GP
Raghava
D000595:Amino Acid Sequence; D003198:Computer Simulation; D008958:Models, Molecular; D015202:Protein Engineering; D017510:Protein Folding; D017433:Protein Structure, Secondary; D012372:ROC Curve; D012984:Software
analysis of beta turn residue; beta turn prediction; beta turn type prediction; designing of beta turn; statistical based beta turn prediction