Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation

Author(s): Chun Li*, Jialing Zhao, Changzhong Wang, Yuhua Yao

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 21 , Issue 2 , 2018

Become EABM
Become Reviewer
Call for Editor


Aim and Objective: The rapid increase in the amount of protein sequence data available leads to an urgent need for novel computational algorithms to analyze and compare these sequences. This study is undertaken to develop an efficient computational approach for timely encoding protein sequences and extracting the hidden information.

Methods: Based on two physicochemical properties of amino acids, a protein primary sequence was converted into a three-letter sequence, and then a graph without loops and multiple edges and its geometric line adjacency matrix were obtained. A generalized PseAAC (pseudo amino acid composition) model was thus constructed to characterize a protein sequence numerically.

Results: By using the proposed mathematical descriptor of a protein sequence, similarity comparisons among β-globin proteins of 17 species and 72 spike proteins of coronaviruses were made, respectively. The resulting clusters agreed well with the established taxonomic groups. In addition, a generalized PseAAC based SVM (support vector machine) model was developed to identify DNA-binding proteins. Experiment results showed that our method performed better than DNAbinder, DNA-Prot, iDNA-Prot and enDNA-Prot by 3.29-10.44% in terms of ACC, 0.056-0.206 in terms of MCC, and 1.45-15.76% in terms of F1M. When the benchmark dataset was expanded with negative samples, the presented approach outperformed the four previous methods with improvement in the range of 2.49-19.12% in terms of ACC, 0.05-0.32 in terms of MCC, and 3.82- 33.85% in terms of F1M.

Conclusion: These results suggested that the generalized PseAAC model was very efficient for comparison and analysis of protein sequences, and very competitive in identifying DNA-binding proteins.

Keywords: Adjacency matrix, Generalized PseAAC, graph, identification of DNA-binding proteins, phylogenetic analysis, protein sequences.

open access plus

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2018
Page: [100 - 110]
Pages: 11
DOI: 10.2174/1386207321666180130100838

Article Metrics

PDF: 43
PRC: 1