Predicting S-nitrosylation proteins and sites by fusing multiple features

Wang-Ren Qiu; Qian-Kun Wang; Meng-Yue Guan; Jian-Hua Jia; Xuan Xiao; Wang-Ren Qiu; Qian-Kun Wang; Meng-Yue Guan; Jian-Hua Jia; Xuan Xiao

doi:10.3934/mbe.2021450

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 9132-9147. doi: 10.3934/mbe.2021450

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Predicting S-nitrosylation proteins and sites by fusing multiple features

School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China

Academic Editor: William Guo

Received: 06 September 2021 Accepted: 07 October 2021 Published: 25 October 2021

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of $k$-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS
- S-nitrosylation,
- random forest,
- post-translational modification,
- multiple features,
- identification
Citation: Wang-Ren Qiu, Qian-Kun Wang, Meng-Yue Guan, Jian-Hua Jia, Xuan Xiao. Predicting S-nitrosylation proteins and sites by fusing multiple features[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9132-9147. doi: 10.3934/mbe.2021450

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Abstract

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of $k$-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS

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