Prediction of Protein Quaternary Structural Type with Functional Domain and Pseudo Amino Acid Composition

In the protein universe, many proteins are composed of two or more polypeptide chains, generally referred to as subunits, which associate through noncovalent interactions and, occasionally, disulfide bonds. With the number of protein sequences entering into data banks rapidly increasing, we are confronted with a challenge: how to develop an automated method to identify the quaternary attribute for a new polypeptide chain (i.e., whether it is formed just as a monomer, or as a dimer, trimer, or any other oligomer). This is important, because the functions of proteins are closely related to their quaternary attribute. In this report, using machine learning approach, the nearest neighbor algorithm (NNA) and covariant-discriminant algorithm (CDA), we developed a prediction system for protein quaternary structural type in which we incorporated functional domain composition (FunD) and pseudo-amino acid composition (PseAA). To compare, we adopted a benchmark dataset, which had been studied time after time. The overall accuracy achieved by this system is more than 89% in the Jack-knife test. Such a technique should improve the success rate of structural biology projects.