A Study on Novel Amino Acid Pair Features for Protein Evolutionary Classifications

Protein evolutionary classification from amino acid sequence is one of the hot research topics in computational biology and bioinformatics. The amino acid composition and arrangement in a protein sequence embed the hints to its evolutionary origins. The feature extraction from an amino acid sequence to a numerical vector is still a challenging problem. Traditional feature methods extract protein sequence information either from individual amino acids or kmers aspects, which have general performance with limitations in classification accuracy. To further improve the accuracy in protein evolutionary classifications, six new features defined on separated amino acid pairs are proposed for protein evolutionary classification analysis, where composition and arrangement as well as physical properties are considered for the different combinations of separated amino acid pairs. Different from general consideration of amino acid pairs, the new features account for the features of separated amino acid pairs with spatial intervals in the sequence, which may deeper reflect the spatial relationships and characters between the amino acid in pairs. In test of the performances of the new features, five standard protein evolutionary classification examples are employed, where the new features proposed are compared with classical protein sequence features such as averaged property factors (APF), natural vector (NV) and pseudo amino acid composition (PseAAC) as well as kmer versions of these features. The area under precision-recall curve (AUPRC) analysis shows that the new features are efficient in evolutionary classifications, which outperform traditional protein sequence features that are based on individual amino acids and kmers. Parameter analysis on the novel separated amino acid pair features and kmer features show that the features of some medium or longer length of amino acid pair intervals and kmers may achieve higher classification accuracy in evolutionary classifications. From this analysis, the newly proposed separated amino acid pairs with spacial intervals are proved to be efficient units in extracting protein sequences features, which may interpret richer evolutionary information of protein sequences than individual amino acids and kmers.