Construction of protein distance matrix based on amino acid indices and Discrete Fourier Transform

CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences

Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.

Construction of protein dendrograms based on amino acid indices and Discrete Fourier Transform

From the literature, existing methods use pairwise percent identity to identify the percentage of similarity between two protein sequences, in order to create a dendrogram. As this is a parametric method of measuring the similarities between proteins, and different parameter may yield different results, this method does not guarantee that the global optimal similarity values will be found. As protein dendrogram construction is used in other areas, such as multiple protein sequence alignments, it is very important that the most related protein sequences to be identified and align first. Furthermore, by using the pairwise percent identity of the protein sequences to construct the dendrograms, the physical characteristics of protein sequences and amino acids are not considered. In this paper, a new method was proposed for constructing protein sequence dendrograms. For this method, Discrete Fourier Transform, was used to construct the distance matrix in combination with the multiple amino acid indices that were used to encode protein sequences into numerical sequences. In order to show the applicability and robustness of the proposed method, a case study was presented by using nine Cluster of Differentiation 4 protein sequences extracted from the UniProt online database.