title: A balanced secondary structure predictor.
abstract: Secondary structure (SS) refers to the local spatial organization of a polypeptide backbone atoms of a protein. Accurate prediction of SS can provide crucial features to form the next higher level of 3D structure of a protein accurately. SS has three different major components, helix (H), beta (E) and coil (C). Most of the SS predictors express imbalanced accuracies by claiming higher prediction performances in predicting H and C, and on the contrary having low accuracy in E predictions. E component being in low count, a predictor may show very good overall performance by over-predicting H and C and under predicting E, which can make such predictors biologically inapplicable. In this work we are motivated to develop a balanced SS predictor by incorporating 33 physicochemical properties into 15-tuble peptides via Chou׳s general PseAAC, which allowed obtaining higher accuracies in predicting all three SS components. Our approach uses three different support vector machines for binary classification of the major classes and then form optimized multiclass predictor using genetic algorithm (GA). The trained three binary SVMs are E versus non-E (i.e., E/¬E), C/¬C and H/¬H. This GA based optimized and combined three class predictor, called cSVM, is further combined with SPINE X to form the proposed final balanced predictor, called MetaSSPred. This novel paradigm assists us in optimizing the precision and recall. We prepared two independent test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. MetaSSPred significantly increases beta accuracy (QE) for both the datasets. QE score of MetaSSPred on CB471 and N295 were 71.7% and 74.4% respectively. These scores are 20.9% and 19.0% improvement over the QE scores given by SPINE X alone on CB471 and N295 datasets respectively. Standard deviations of the accuracies across three SS classes of MetaSSPred on CB471 and N295 datasets were 4.2% and 2.3% respectively. On the other hand, for SPINE X, these values are 12.9% and 10.9% respectively. These findings suggest that the proposed MetaSSPred is a well-balanced SS predictor compared to the state-of-the-art SPINE X predictor.
authors.affiliation: Computer Science, University of New Orleans, Louisiana 70148, USA.
authors.forename: Md
authors.initials: M
authors.lastname: Nasrul Islam
authors.affiliation: Computer Science, University of New Orleans, Louisiana 70148, USA.
authors.forename: Sumaiya
authors.initials: S
authors.lastname: Iqbal
authors.affiliation: National Cancer Institute, National Institute of Health (NIH), USA.
authors.forename: Ataur R
authors.initials: AR
authors.lastname: Katebi
authors.affiliation: Computer Science, University of New Orleans, Louisiana 70148, USA. Electronic address: thoque@uno.edu.
authors.forename: Md
authors.initials: M
authors.lastname: Tamjidul Hoque
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016333:HIV Protease / Q000737:chemistry; D020407:Internet; D011336:Probability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D016333:HIV Protease; C514803:p16 protease, Human immunodeficiency virus 1
keywords: Balanced accuracy; Meta predictor; Secondary structure prediction; Support vector machine