Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Li-Ping Li,Peng-Wei Hu,Zhan-Heng Chen,Yu Qiu,Zhu-Hong You,Yan-Bin Wang

doi:10.1186/s12864-019-6301-1

Abstract

BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

Highlights

Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes
As follows: first of all, the Position Specific Scoring Matrix (PSSM) of each protein sequence is converted into a 400-dimensional feature vector by employing Finite Impulse Response Filter (FIRF) method to extract helpful information; to remove the influence of noise, we reduced the dimension from 400 to 300 by applying Principal Component Analysis (PCA) method; At last, realized classification on yeast and human datasets by relying on Random Projection (RP) classifier
Aiming at the fairness and over-fitting problems, we repeated the experiment five times on the two same datasets, termed five-fold cross validation. Described it in details, we split the human dataset which was mainly composed of characteristic values into five non-overlapping pieces, and four parts was randomly chosen as training set and selected the remaining characteristic values as independent test set

Summary

Results

We developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. To effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches

Conclusion

Background

Results and discussion