Next Article in Journal
A Two-Step Ferric Chloride and Dilute Alkaline Pretreatment for Enhancing Enzymatic Hydrolysis and Fermentable Sugar Recovery from Miscanthus sinensis
Previous Article in Journal
Multivalent Ions as Reactive Crosslinkers for Biopolymers—A Review
Open AccessArticle

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

by Da Xu 1, Hanxiao Xu 1, Yusen Zhang 1,*, Wei Chen 1 and Rui Gao 2
1
School of Mathematics and Statistics, Shandong University, Weihai 264209, China
2
School of Control Science and Engineering, Shandong University, Jinan 250061, China
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(8), 1841; https://doi.org/10.3390/molecules25081841
Received: 31 March 2020 / Revised: 13 April 2020 / Accepted: 14 April 2020 / Published: 16 April 2020
(This article belongs to the Section Computational and Theoretical Chemistry)
Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods. View Full-Text
Keywords: protein-protein interaction; graph energy; physicochemical properties; contact information; WSRC classifier protein-protein interaction; graph energy; physicochemical properties; contact information; WSRC classifier
Show Figures

Figure 1

MDPI and ACS Style

Xu, D.; Xu, H.; Zhang, Y.; Chen, W.; Gao, R. Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information. Molecules 2020, 25, 1841.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop