Capturing hydrophobic moment using spectral coherence for protein secondary structure prediction

The endeavor to decipher the structure and function of a protein from its amino acid sequence has provided an enduringly interesting challenge. Due to the sheer quantity of existing protein data, this challenge naturally presents itself as a complex computational problem requiring the deployment of novel data mining techniques. We hypothesize that the hydrophobic moment (HM) is important in the folding and the formation of secondary structures, and is evolutionarily retained in structurally related proteins. We propose the use of magnitude-squared spectral coherence (MSC) to capture HM of a sequence using selected hydrophobicity scales for effective structural and fold classification of protein sequences. Extensive experimentation on PDBselect dataset demonstrates overall success rates of 77.4% and 63.4% for structural and fold classification. The comparative results show that spectral coherence between the hydrophobic and hydrophilic representations of a sequence effectively captures periodic hydrophobic variations over the length of the sequence that corresponds to HM.