Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition

• Published in: Journal of theoretical biology Vol. 380; pp. 291 - 298
• Main Authors: Saini, Harsh, Raicar, Gaurav, Sharma, Alok, Lal, Sunil, Dehzangi, Abdollah, Lyons, James, Paliwal, Kuldip K., Imoto, Seiya, Miyano, Satoru
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07.09.2015
• Subjects: Amino Acids - chemistry; Binding Sites; Datasets as Topic; Dimerization; Probability; Protein Folding; Proteins - chemistry
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2015.05.030

Identification of the tertiary structure (3D structure) of a protein is a fundamental problem in biology which helps in identifying its functions. Predicting a protein׳s fold is considered to be an intermediate step for identifying the tertiary structure of a protein. Computational methods have been applied to determine a protein׳s fold by assembling information from its structural, physicochemical and/or evolutionary properties. In this study, we propose a scheme in which a feature extraction technique that extracts probabilistic expressions of amino acid dimers, which have varying degree of spatial separation in the primary sequences of proteins, from the Position Specific Scoring Matrix (PSSM). SVM classifier is used to create a model from extracted features for fold recognition. The performance of the proposed scheme is evaluated against three benchmarked datasets, namely the Ding and Dubchak, Extended Ding and Dubchak, and Taguchi and Gromiha datasets. The proposed scheme performed well in the experiments conducted, providing improvements over previously published results in literature. •Relationships between amino acid dimers that may be non-adjacent in sequence are explored.•Features are extracted directly from PSSM instead of raw counts from primary sequence.•SVM is used for classification.•Achieved good results on Ding and Dubchak, Extended Ding and Dubchak, and Taguchi and Gromhia datasets.