Kumar, Manish and Bhasin, Manoj and Natt, Navjot K and Raghava, G.P.S. (2005) BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. Nucleic acids research, 33 (Web Se). W154-9. ISSN 1362-4962
|
PDF (OPEN ACCESS)
raghava2005.4.pdf - Published Version Available under License Creative Commons Attribution. Download (93Kb) | Preview |
Abstract
This paper describes a method for predicting a supersecondary structural motif, beta-hairpins, in a protein sequence. 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 beta-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. The performances of the methods developed were assessed in a test case, where predicted secondary structure and su
