Prediction of Protein–Protein Interactions with Clustered Amino Acids and Weighted Sparse Representation

International Journal of Molecular Sciences

Qiaoying Huang¹  Zhuhong You²  Xiaofeng Zhang¹  Yong Zhou² 

[1] Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China; E-Mails:;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; E-Mail:

关键词: reduced amino acid alphabet;    weighted sparse representation-based classification;    protein–protein interactions;   

DOI  :  10.3390/ijms160510855

来源: mdpi

【 摘 要 】

With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein–protein interactions (PPIs) research is becoming more and more important. Life activities and the protein–protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.