A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties

Mehri Mahmoodi-Reihani, Fatemeh Abbasitabar and Vahid Zare-Shahabadi

Physica A: Statistical Mechanics and its Applications, 2018, vol. 510, issue C, 477-485

Abstract: One of popular topic in bioinformatics is protein sequence analysis. The graphical representation of protein sequence is a simple and common way to visualize protein sequences. In this study, a numerical descriptive vector for a given protein sequence is calculated based on twelve physicochemical properties of amino acids (AAs) and principal component analysis (PCA). Each entry of the descriptive vector corresponds to one AA in the sequence. By this vector, an intuitive spectrum-like graphical representation of protein sequence is proposed. Squared correlation coefficient as well as moving window correlation coefficient, as a new similarity/dissimilarity measure, were used to compare different sequences. Applicability of the proposed method is assessed by analyzing the nine ND5 proteins. The results revealed the utility of the proposed method.

Keywords: Protein sequence; Graphical representation; Principal component analysis; Physicochemical property; Moving window correlation coefficient (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:510:y:2018:i:c:p:477-485

DOI: 10.1016/j.physa.2018.07.011

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