Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures
Kountouris, Peter; Hirst, J.D.
Professor JONATHAN HIRST JONATHAN.HIRST@NOTTINGHAM.AC.UK
Professor of Computational Chemistry
Background: β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.
Results: We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and “non-specific”, achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.
Conclusions: We have created an accurate predictor of b-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.
Kountouris, P., & Hirst, J. (2010). Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures. BMC Bioinformatics, 11,
|Journal Article Type||Article|
|Publication Date||Jan 1, 2010|
|Deposit Date||Jan 26, 2011|
|Publicly Available Date||Jan 26, 2011|
|Peer Reviewed||Peer Reviewed|
|Related Public URLs||http://comp.chem.nottingham.ac.uk/debt/|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0|
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
You might also like
Force Fields for Macromolecular Assemblies Containing Diketopyrrolopyrrole and Thiophene
Unfolding Dynamics of a Photoswitchable Helical Peptide
Dewar Benzenoids Discovered in Carbon Nanobelts