Complex Resonant Recognition Model in analysing Influenza a virus subtype protein sequences

Abstract

Resonant Recognition Method that uses discrete Fourier transform (DFT) and Electron-ion interaction potential amino acid scale (EIIP) is one of the techniques widely used for the analysis of protein sequences. However, DFT that generates complex output (imaginary and real frequency spectra) has shown to produce complementary information in other areas (e.g., ultrasound) were not taken into consideration. Therefore, for the first time, this study is concerned with the development of complex resonant recognition method (CRRM) for the analysis of groups of proteins using their sequence information. As a case study, the method developed is applied to extract characteristic frequency peaks of Influenza A subtypes Neuraminidase gene, for which Influenza A virus subtypes H1N1, H2N2, H3N2 and H5N1 proteins were extracted from Influenza Virus Resource database. The relationships of Influenza A subtypes that appear in CRRM real and imaginary spectra are found to be consistent to the biological link whereas this was not observed in the traditional RRM. H3N2 inherited NA gene from H2N2 and they are found to share the same characteristic frequency as seen in the real spectrum. In addition, H1N1 supplied the NA gene to H5N1 and they also have the same characteristic frequency in the imaginary spectrum. The results clearly show that imaginary part of the CRRM clearly identified similarities and differences between the influenza sub-types at the proteomic level where real part and absolute value of the DFT were incapable of doing so. The results obtained for this study therefore suggest that the CRRM cannot only produce additional biological information but also helps better distinguish biological differences between the families of the proteins. This is hence expected to help better understand mechanisms of the diseases and aid drug/vaccine development.