Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm
- 82 Downloads
Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases.
KeywordsPosition-specific scoring matrix Jackknife test Support vector machine Isometric mapping Apoptosis proteins
The authors thank the editors and the anonymous reviewers for their careful works and valuable suggestions for this study. This research was financially supported by the National Natural Science Foundation of China (grant nos. 61463052, 61365001), and the 10th Research Innovation Project of Yunnan University of China (no. 2018Z081), and Yunnan Province University Key Laboratory Construction Plan Funding, China (no. 2019Y0003).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 3.Kuo HH, Ahmad R, Lee GQ, Gao C, Chen HR, Ouyang Z, Szucs MJ, Kim D, Tsibris A, Chun TW, Battivelli E, Verdin E, Rosenberg ES, Carr SA, Yu XG, Lichterfeld M (2018) Anti-apoptosis protein BIRC5 maintains survival of HIV-1-infected CD4 + T cells. Immunity 48(6):1183–1194PubMedPubMedCentralCrossRefGoogle Scholar
- 7.Lumbroso D, Soboh S, Avi M et al (2018) Macrophage-derived protein s facilitates apoptosis polymorphonuclear cell clearance by resolution phase macrophages and supports their reprogramming. Front Immunol 9(358):1–10Google Scholar
- 9.Zhou H, Yang Y, Shen HB (2016) Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Bioinformatics 33(6):843–853Google Scholar
- 15.Paliwal K, Heffernan R, Hanson J et al (2018) Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform 3(19):482–494Google Scholar
- 33.Chen PF, Zhao RZ, Peng B et al (2017) Method for the dimension reduction of rotor fault data sets by using ISOMAP and LLE. J Vibr Shock 36(6):45–50 and 156Google Scholar
- 37.Backenroth D, He ZH, Kiryluk K, Boeva V, Pethukova L, Khurana E, Christiano A, Buxbaum JD, Ionita-Laza I (2018) FUN-LDA: a latent dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: methods and applications. Am J Hum Genet 102(5):920–942PubMedPubMedCentralCrossRefGoogle Scholar
- 41.Chen YL, Li QZ (2004) Prediction of the subcellular location of apoptosis proteins using the algorithm of measure of diversity. Acta Scientiarum Naturalium Universitatis Neimongol 35(4):413–417Google Scholar
- 42.Huang J, Shi F, Zhou HB (2005) Support vector machine for predicting apoptosis proteins types by incorporating protein instability index. Bioinformatiocs 3(3):121–123Google Scholar
- 49.Liang YY, Liu SY, Zhang SL (2016) Geary autocorrelation and DCCA coefficient: application to predict apoptosis protein subcellular localization via PSSM. PHYSICA A 467(2017):296–306Google Scholar