An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition

Abstract

In this paper, we propose a method to create the 60-dimensional feature vector for protein sequences via the general form of pseudo amino acid composition. The construction of the feature vector is based on the contents of amino acids, total distance of each amino acid from the first amino acid in the protein sequence and the distribution of 20 amino acids. The obtained cosine distance metric (also called the similarity matrix) is used to construct the phylogenetic tree by the neighbour joining method. In order to show the applicability of our approach, we tested it on three proteins: 1) ND5 protein sequences from nine species, 2) ND6 protein sequences from eight species, and 3) 50 coronavirus spike proteins. The results are in agreement with known history and the output from the multiple sequence alignment program ClustalW, which is widely used. We have also compared our phylogenetic results with six other recently proposed alignment-free methods. These comparisons show that our proposed method gives a more consistent biological relationship than the others. In addition, the time complexity is linear and space required is less as compared with other alignment-free methods that use graphical representation. It should be noted that the multiple sequence alignment method has exponential time complexity.

A new method to analyze protein sequence similarity using Dynamic Time Warping

“…As a comparison, we apply the method in Ref. [21] to analyze the dataset in Table 2. The results are shown in Fig.…”

“…We use some coronavirus spike proteins as inputs to test our method. All the data comes from the Table 3 in reference [21]. The relations revealed in Fig.…”

“…Considering a protein sequence consists of 20 kinds of different amino acids while a DNA sequence only consists of four bases, it is much more complex to express a protein than a DNA sequence. However, there are some methods which are generalized from the ways of analyzing the DNA sequences [17][18][19][20][21]. Yau et al propose a method with the name of protein map [22] following their previous work.…”

Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

“…However, as elucidated in [38] and demonstrated by Eqs. (28)-(32) of [38], among the three cross-validation methods, the jackknife test is deemed the least arbitrary (most objective) that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (see, e.g., [46,[52][53][54][55][61][62][63][64][65][66][67][68][69][70]). Accordingly, the jackknife test is also adopted here to examine the quality of the present predictor.…”

Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

“…The concept of Chou's PseAAC was presented in 2001 and then it quickly pierced into many areas of computational proteomics [23,24,[58][59][60][61]. A flexible web server creates a variety of protein PseAACs (http://chou.med.harvard.edu/bioinf/PseAAC).…”