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. 2021 Aug 19;11(1):16910.
doi: 10.1038/s41598-021-96265-z.

Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information

Yang Li  1 , Zheng Wang  2 , Li-Ping Li  3 , Zhu-Hong You  1 , Wen-Zhun Huang  1 , Xin-Ke Zhan  1 , Yan-Bin Wang  4
Affiliations

Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information

Yang Li et al. Sci Rep. .

Abstract

Various biochemical functions of organisms are performed by protein-protein interactions (PPIs). Therefore, recognition of protein-protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The workflow of the proposed method.
Figure 2
Figure 2
The accuracy surface obtained from the RoF algorithm for optimizing parameters K and L.
Figure 3
Figure 3
ROC curves performed using the proposed method on Yeast dataset.
Figure 4
Figure 4
ROC curves performed using the proposed method on Human dataset.
Figure 5
Figure 5
ROC curves performed using the SVM method on Yeast dataset.
Figure 6
Figure 6
ROC curves performed using the SVM method on Human dataset.
See this image and copyright information in PMC

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