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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy

Author(s): Zhaohui Qi* and Xinlong Wen

Volume 25, Issue 3, 2022

Published on: 01 September, 2020

Page: [392 - 400] Pages: 9

DOI: 10.2174/1386207323666200901103001

Price: $65

Abstract

Aim and Objective: Aim and Objective: Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metrics hidden in the sequence. Otherwise, the graphical representation of the biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences.

Materials and Methods: The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from the transition probability graph to transition probability vector by the k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.

Results: By constructing a phylogenetic tree, it was found that the results of each application are consistent with other studies.

Conclusion: The graphical representation proposed in this article is a practical and correct method.

Keywords: Graphical bioinfomatics, similarity, sequence, descriptors, transition probability graph, information entropy.

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Graphical Abstract

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