Abstract
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.
Highlights
If it is considered that a protein’s biological function is controlled by a selective ability of the protein to interact with selected elements in the environment, the following argument arises: how is this selective ability achieved? Several attempts have been made to decode such characteristic features that help drive biological functions of the proteins directly from primary structure of a protein sequence
The results produced from Complex informational spectrum analysis for protein sequences (CISAPS) were ranked according to the similarities and differences based on standard deviation (STD) [38]
The thresholds used for the results presented in this paper are for amino acid indices (AAI) with identical or closely related characteristic frequency peak (CFP) (Tables 1, 2, and 3) smaller than 0.01 and for AAI that present the most distinct results (Tables 4, 5, and 6), larger than 0.2
Summary
If it is considered that a protein’s biological function is controlled by a selective ability of the protein to interact with selected elements in the environment, the following argument arises: how is this selective ability achieved? Several attempts have been made to decode such characteristic features that help drive biological functions of the proteins directly from primary structure of a protein sequence. One example is the basic local alignment search tool (BLAST) [1] Another method for analysing macromodule sequences is to extract structural and physicochemical features, such as amino acid composition and dipeptide composition derived from the primary structure of a protein sequence. Every biological function corresponds to one unique or a set of unique peaks The importance of this general conclusion is that specific biological functions can be extracted from protein sequences using signal processing techniques by identifying significant features of the frequencies which are not found in unrelated frequencies. Complementary information such as real and imaginary frequency spectra can be derived from
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