Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments
Beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality.
The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor.
Results: We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies.
The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods.
Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method.
The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively.
Conclusions: Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between beta-turns and non-beta-turns due to obtaining lower numbers of false positive predictions.
The prediction model and datasets are freely available at http://biomine.ece.ualberta.ca/BTNpred/BTNpred.html.
Author: Ce Zheng and Lukasz Kurgan
Credits/Source: BMC Bioinformatics 2008, 9:430
Published on: 2008-10-10
Limited copyright is granted for you to use and/or republish any story on this site for
any legitimate media purpose as long as you reference 7thSpace and any source mentioned in the story above. Please
make sure to read our disclaimer prior to contacting 7thSpace Interactive. To contact our editors, visit our online helpdesk. If you wish submit your own press release, click here.
Social Bookmarking
Digg this! | Post to del.icio.us | Post to Furl | Add to Netscape | Add to Yahoo! | Rojo
|
|
