Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures

Abstract

Background: Beta-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 beta-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 beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-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 beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Figure 1

The architecture of our β-turn location and β-turn type prediction method. An example of an input sequence is provided at the top. Around each residue to be predicted (shown in red), two local windows are used. One, l₁, has a size of nine residues and is used for the PSSM values, while the other, l₂, takes in account the predicted secondary structures and dihedral angles for five residues. After running PSI-BLAST [46], the PSSM values are linearly scaled and transformed into a vector of 180 attributes (i.e. a local window of nine residues, l₁). DISSPred [5] utilises PSSMs to predict three-state secondary structures and seven-state dihedral angles, which are transformed into a vector of 50 attributes using a window of five residues (l₂). The two vectors are merged to create the final input vector for the SVM classifiers. Lastly, the predictions are filtered to give the final result.

Figure 2

ROC curves for the prediction on the GR426 dataset. Dashed curves correspond to the PSSM-only prediction, while solid curves correspond to the prediction after augmenting the input vector with predicted dihedral angles and secondary structures.

Figure 3

An example of an output file produced in DEBT web-server. The first and second columns show the one-letter code and the number of the amino acids, respectively. Column three shows the prediction value of the turn/non-turn prediction and columns four to eight show the prediction values for β-types I, II, IV, VIII and NS, respectively. A prediction value can be "1" if the corresponding residue is predicted in β-turn/β-turn type and "0" otherwise.