title: Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.
abstract: Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets.
authors.affiliation: The University of the South Pacific, Fiji. Electronic address: raicar_g@usp.ac.fj.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Raicar
authors.affiliation: The University of the South Pacific, Fiji. Electronic address: saini_h@usp.ac.fj.
authors.forename: Harsh
authors.initials: H
authors.lastname: Saini
authors.affiliation: University of Iowa, USA. Electronic address: i.dehzangi@gmail.com.
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
authors.affiliation: Massey University, New Zealand. Electronic address: s.lal@massey.ac.nz.
authors.forename: Sunil
authors.initials: S
authors.lastname: Lal
authors.affiliation: The University of the South Pacific, Fiji; IIIS, Griffith University, Australia; RIKEN, Japan. Electronic address: sharma_al@usp.ac.fj.
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D055598:Chemical Phenomena; D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods*; D056510:Position-Specific Scoring Matrices; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: Evolutionary-based features; Forward consecutive search scheme; Physicochemical properties; Protein fold recognition; Structural class prediction; Syntactical-based features