title: An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids.
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.
authors.affiliation: School of Science, Yanshan University, Qinhuangdao, China.
authors.forename: Yushuang
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Science, Yanshan University, Qinhuangdao, China.
authors.forename: Tian
authors.identifier: http://orcid.org/0000-0001-9225-3416
authors.initials: T
authors.lastname: Song
authors.affiliation: Department of Civil and Environmental Engineering, National Universality of Singapore, Singapore.
authors.forename: Jiasheng
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Mathematics, Hebei University of Science and Technology, Shijiazhuang, Hebei, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Mathematics and Information Science, Henan Polytechnic University, Henan, China.
authors.forename: Jialiang
authors.identifier: http://orcid.org/0000-0003-4689-8672
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D006026:Glycoside Hydrolases / Q000737:chemistry; D011336:Probability; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D000596:Amino Acids; D011506:Proteins; D006026:Glycoside Hydrolases