BhairPred -- prediction of xdf-hairpins in a protein from multiple alignment information using ANN and SVM techniques
What you can do:
Predict a super secondary structural motif, xdf-hairpins, in a protein sequence.
Highlights:
- BhairPred has been developed to predict xdf-hairpins in a protein using the SVM approach.
- The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs.
- Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict xdf-hairpins.
- An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input.
- The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs.
- The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN.
Keywords:
- protein secondary structure prediction tool
- protein sequence analysis tool
- protein super structural motif prediction tool
Literature & Tutorials:
This record last updated: 09-22-2005