Measuring Similarity among Protein Sequences Using a New Descriptor

Abstract

The comparison of protein sequences according to similarity is a fundamental aspect of today's biomedical research. With the developments of sequencing technologies, a large number of protein sequences increase exponentially in the public databases. Famous sequences' comparison methods are alignment based. They generally give excellent results when the sequences under study are closely related and they are time consuming. Herein, a new alignment-free method is introduced. Our technique depends on a new graphical representation and descriptor. The graphical representation of protein sequence is a simple way to visualize protein sequences. The descriptor compresses the primary sequence into a single vector composed of only two values. Our approach gives good results with both short and long sequences within a little computation time. It is applied on nine beta globin, nine ND5 (NADH dehydrogenase subunit 5), and 24 spike protein sequences. Correlation and significance analyses are also introduced to compare our similarity/dissimilarity results with others' approaches, results, and sequence homology.

HELIOS: High-speed sequence alignment in optics

“…To perform quantitative measurement of homology [ 51 ], the parameters Identity, Similarity, and Alignment Score of the HELIOS outputs are calculated through simulation studies, as reported in Tables 1 – 3 , respectively, assuming the “Nine ND5 protein sequences dataset” [ 53 ]. While the Identity reports the number of exactly matched characters of two sequences (in percentage), the Similarity measures the resemblance of two compared sequences.…”

“…Additionally, we consider twelve different datasets for this evaluation, represented in S1 Text – S12 Text ; while the input sequences of each dataset are represented in Table A3 in its corresponding file. Moreover, the quantitative measurement of homology of all aforementioned algorithms are reported in Tables A4-A33 in the S1 Text – S12 Text for twelve different datasets [ 53 , 55 – 61 ]. By the way, as a brief report, the average value of each parameter, achieved by the aforementioned algorithms, are reported in Table 7 for the twelve datasets.…”

“…See S1 Text for the detailed results. 2 Dataset 2: Nine beta globin protein sequences dataset [ 53 ]. See S2 Text for the detailed results.…”

Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA

“…The construction of these graphic curves is based on the allocation of individual bases of four different sine (or tangent) functions. In 2019, Abo-Elkhier et al numerically represented each amino acid in the protein sequence and proposed a new 2-D graphical representation method [17]. They introduced a new descriptor that consisted of a vector (Ā t , SA t ) consisting of the mean and standard deviation from the total number of protein sequences.…”