OSDDlinux for Standalone, Galaxy & Local version

BetaTurns: Prediction of beta-turn types

The aim of Betaturns server is to predict different types of turns such as Types I, II, IV, VIII and non-specific in a given amino acid sequence. The method is based on neural network. It uses two feed-forward back-propagation neural networks with a single hidden layer, where the first sequence-to-structure network is trained on PSI-BLAST generated position specific matrices (Altschul et al. 1997). The second structure-to-structure network is trained on the outputs obtained from first network and PSIPRED predicted secondary structure information (Jones 1999). It has been trained and tested on a data set of 2881 sequence unique proteins using five-fold cross-validation technique.

The results show that Type I and II beta-turns have better prediction performance than type IV and VIII beta-turn types. The final network yields an overall accuracy of 73.6%, 91.6%, 52.9% and 92.4% and MCC values 0.25, 0.36, 0.05 and 0.10 for Type I, II, IV and VIII beta-turns respectively and is the highest achieved so far.

The input to the server is a single-letter code amino acid sequence in fasta or free format. The server predicts the beta-turn types in two steps. In first step, the residues forming beta-turns are predicted by using BetaTPred2(Kaur and Raghava 2003). The BetaTPred2 predicted beta-turns are further classified into different types using betaturns server. The output consists of target sequence, PSIPRED predicted secondary structure (helix:'H', beta-sheet:'E' and coil:'C',). Turn residues are predicted as 4 residues block with turn types indicated by roman numerals I, II, IV, VIII for turn types I, II, IV and VIII respectively or 'NS' for non-specific beta-turn category which does not belong to any of the 4 turn types.

This web server is based on following publication, please cite if you are using this web server

Kaur,H. and Raghava, G. P. S. (2004) A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 20:2751-8.