Improving protein complex classification accuracy using amino acid composition profile.
Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.
Department of Computer Science and Information Engineering, National Formosa University, 64, Wen-Hwa Road, Hu-wei, Yun-Lin 632, Taiwan.
Chien-Hung
CH
Huang
Szu-Yu
SY
Chou
Ka-Lok
KL
Ng
D006801:Humans; D011237:Predictive Value of Tests; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000145:classification* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
D029701:Saccharomyces cerevisiae Proteins
Amino acid composition profile; Gene Ontology; Hydrophilic; Hydrophobic; Machine learning method; Physicochemical property; Protein complex; Protein–protein interaction; Sequence alignment