Predicting protein structural classes with pseudo amino acid composition: A new approach using geometric moments of distance matrix image

摘要

Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. In this paper, the protein distance matrix image(DMI) is introduced. Based on the protein DMI, two geometric moments derived form each of the protein sequences concerned are adopted for its PseAA. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was significantly higher than those by the others. The remarkable merit of this approach is that many image recognition tools can be straightforwardly utilized in predicting protein structural classes.