Abstract
In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector.
Highlights
With the recent development of next-generation sequencing technologies, there has been an explosion in the numbers of available DNA and protein sequences
We proposed a novel representation for a protein sequence based on the two features, i.e. a 440-D feature vector consisting of (1) a 400-D Pseudo-Markov transition probability vector reflecting the order information of adjacent amino acids. (2) a 20-D amino acid content ratio vector describing the frequency of each amino acid in the sequence, and (3) a 20-D amino acid position ratio vector exhibiting the position distribution of each amino acid
The ND5 dataset consists of the ND5 protein sequences of 9 species including human, gorilla, pigmy chimpanzee, common chimpanzee, fin whale, blue whale, rat, mouse, and opossum (Table 1)
Summary
With the recent development of next-generation sequencing technologies, there has been an explosion in the numbers of available DNA and protein sequences. The numerous newly sequenced protein sequences present an urgent need for novel computational algorithms to compare their similarities with sequences from known protein families, to predict their structures, and to infer their functions [1,2,3,4,5,6]. As usually the first step in a bioinformatics pipeline, sequence comparison is very crucial since it affects all down-stream analyses. Comparing Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids. Foundation for Advanced Talents (No A201400121 to YZ), the Educational Commission of Hebei Province on of Humanities and Social Sciences(No SZ16180 to YZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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