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Medical & Biological Engineering & Computing

, Volume 57, Issue 12, pp 2553–2565 | Cite as

Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm

  • Xiaoli Ruan
  • Dongming ZhouEmail author
  • Rencan Nie
  • Ruichao Hou
  • Zicheng Cao
Original Article
  • 82 Downloads

Abstract

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.

Graphical abstract

Framework of the proposed prediction model

Keywords

Position-specific scoring matrix Jackknife test Support vector machine Isometric mapping Apoptosis proteins 

Notes

Acknowledgments

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.

References

  1. 1.
    Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins, Proteins Struct. Funct Genet 50(1):44–48CrossRefGoogle Scholar
  2. 2.
    Chen YL, Li QZ (2007) Prediction of the subcellular location of apoptosis proteins. J Theor Biol 245(4):775–783CrossRefGoogle Scholar
  3. 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
  4. 4.
    Pohl SO, Aqositino M, Dhamarajan A et al (2018) Crosstalk between cellular redox state and the anti-apoptosis protein Bcl-2. Antioxid Redox Signal 29(13):1215–1236PubMedCrossRefGoogle Scholar
  5. 5.
    Hasan MA, Ahmad S, Molla MK (2017) Protein subcellular localization prediction using multiple kernel learning based support vector machine. Mol BioSyst 13(4):785–795PubMedCrossRefGoogle Scholar
  6. 6.
    Shu BS, Jia JW, Zhang JJ, Sethuraman V, Yi X, Zhong G (2018) DnaJ homolog subfamily a member1 (DnaJ1) is a newly discovered anti-apoptosis protein regulated by azadirachtin in Sf9 cells. BMC Genomics 19(1):413–424PubMedPubMedCentralCrossRefGoogle Scholar
  7. 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
  8. 8.
    Arpital B, Sarmishtha R, Supriyo C et al (2018) Evaluating the antimicrobial, apoptosis, and cancer cell gene delivery properties of protein-capped gold nanoparticles synthesized from the edible mycorrhizal fungus tricholoma crassum. Nanoscale Res Lett 13(1):154–170CrossRefGoogle Scholar
  9. 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
  10. 10.
    Almagro AJ, Aonderby CK, Sonderby SK et al (2017) DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics 33(21):3387–3395CrossRefGoogle Scholar
  11. 11.
    Khan AA, Khan ZK, Kalam MA et al (2018) Inter-kingdom prediction certainty evaluation of protein subcellular localization tools: microbial pathogenesis approach for deciphering host microbe interaction. Brief Bioinform 19(1):12–22PubMedGoogle Scholar
  12. 12.
    Lópezbegines S, Planabonamaisó A, Méndez A (2018) Molecular determinants of guanylate cyclase activating protein subcellular distribution in photoreceptor cells of the retina. Sci Rep 8(1):2903–2915CrossRefGoogle Scholar
  13. 13.
    Zhang SL, Duan X (2017) Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437(2017):239–250PubMedGoogle Scholar
  14. 14.
    Qiu JD, Luo SH, Huang JH, Sun XY, Liang RP (2010) Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine. Amino Acids 38(4):1201–1208PubMedCrossRefGoogle Scholar
  15. 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
  16. 16.
    Wang T, Yun JH, Xie Y et al (2017) Finding RNA-protein interaction sites using HMMs. Methods Mol Biol 1552:177–184.  https://doi.org/10.1007/978-1-4939-6753-7_13 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Mandal M, Mukhopadhyay A, Maulik U (2015) Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC. Med Biol Eng Comput 53(4):331–344PubMedCrossRefGoogle Scholar
  18. 18.
    Xiang QL, Liao B, Li X, Xu H, Chen J, Shi Z, Dai Q, Yao Y (2017) Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine[J]. Artif Intell Med 78(2017):41–46PubMedCrossRefGoogle Scholar
  19. 19.
    Wang X, Li H, Wang R, Zhang Q, Zhang W, Gan Y (2017) MultiP-Apo: a multilabel predictor for identifying subcellular locations of apoptosis proteins. Comput Intell Neurosci 2017:1–10.  https://doi.org/10.1155/2017/9183796 CrossRefGoogle Scholar
  20. 20.
    Tan YT, Rosdi BA (2015) FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy k-nearest neighbors with lempel–ziv complexity-based distance measure. Neurocomputing 148(148):409–419CrossRefGoogle Scholar
  21. 21.
    Xia B, Zhagn H, Li QM et al (2015) PETs: a stable and accurate predictor of protein-protein interacting sites based on extremely-randomized trees. IEEE Trans Nanobioscience 14(8):882–893PubMedCrossRefGoogle Scholar
  22. 22.
    Cardon LR, Stormo GD (1992) Expectation maximization algorithm for identifying protein-binding sites with variable lengths from unaligned DNA fragments. J Mol Biol 223(1):159–170PubMedCrossRefGoogle Scholar
  23. 23.
    Jia JH, Liu Z, Xiao X, Liu B, Chou KC (2016) iPPBS-Opt: a sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules 21(1):95–114PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Liang YY, Zhang SS (2018) Prediction of apoptosis protein’s subcellular localization by fusing two different descriptors based on evolutionary information. Acta Biotheor 66(1):61–78PubMedCrossRefGoogle Scholar
  25. 25.
    Yu B, Li S, Qiu WY et al (2017) Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget 8(64):107640–107665PubMedPubMedCentralGoogle Scholar
  26. 26.
    Ying LC, Qian ZL (2007) Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J Theor Biol 248(2):377–381CrossRefGoogle Scholar
  27. 27.
    Zhang ZH, Wang ZH, Zhang ZR, Wang YX (2006) A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine. FEBS Lett 580(26):6169–6174PubMedCrossRefGoogle Scholar
  28. 28.
    Wang G, Dunbrack RL (2003) PISCES: a protein sequence culling server. Bioinformatics 19(12):1589–1591PubMedCrossRefGoogle Scholar
  29. 29.
    Liang YY, Zhang SL (2018) Identify gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence. J Theor Biol 454(7):22–29PubMedCrossRefGoogle Scholar
  30. 30.
    Liu B, Wang SY, Dong QW, Li S, Liu X (2016) Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning. IEEE Trans NanoBiosci 15(4):328–334CrossRefGoogle Scholar
  31. 31.
    Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X (2015) PseDNA-pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation. Mol Inform 34(1):8–17PubMedCrossRefGoogle Scholar
  32. 32.
    Harsh S, Gaurav R, Sunil L et al (2016) A. Protein fold recognition using genetic algorithm optimized voting scheme and profile bigram. J Softw 11(8):756–767CrossRefGoogle Scholar
  33. 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
  34. 34.
    Yang XL, Yang W, Song H, Huang P (2018) Polarimetric SAR image classification using geodesic distances and composite kernels. IEEE J Sel Top Appl Earth Obs Remote Sens 11(5):1606–1614CrossRefGoogle Scholar
  35. 35.
    Huang R, Zhang GP, Chen JL (2018) Semi-supervised discriminant Isomap with application to visualization, image retrieval and classification. Int J Mach Learn Cybern:1–10.  https://doi.org/10.1007/s13042-018-0809-6 CrossRefGoogle Scholar
  36. 36.
    Zobia SH, Erika RE, Reyer Z (2018) Classification of micro-calcification in mammograms using scalable linear fisher discriminant analysis. Med Biol Eng Comput 56(8):1475–1485CrossRefGoogle Scholar
  37. 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
  38. 38.
    Bhat PC, Prosper HB, Sekmen S, Stewart C (2018) Optimizing event selection with the random grid search. Comput Phys Commun 228(2018):245–257CrossRefGoogle Scholar
  39. 39.
    Chou KC, Zhang CT (1995) Prediction of protein structural classes. Crit Rev Biochem Mol Biol 30(4):275–349PubMedCrossRefGoogle Scholar
  40. 40.
    Chou KC, Shen HB (2008) Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 3(2):153–162PubMedCrossRefGoogle Scholar
  41. 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. 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
  43. 43.
    Bulashevska A, Eils R (2006) Predicting protein subcellular locations using hierarchical ensemble of bayesian classifiers based on markov chains. Bmc Bioinformatics 7(1):298–311PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Qiu JD, Luo SH, Huang JH, Sun XY, Liang RP (2010) Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine. Amino Acids 38(4):1201–1208PubMedCrossRefGoogle Scholar
  45. 45.
    Zhang L, Liu B, Li DC et al (2009) A novel representation for apoptosis protein subcellular localization prediction using support vector machine. J Theor Biol 259(2):361–365PubMedCrossRefGoogle Scholar
  46. 46.
    Liu TG, Zheng XQ, Wang CH, Wang J (2010) Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation. Protein Pept Lett 17(10):1263–1269PubMedCrossRefGoogle Scholar
  47. 47.
    Yu XQ, Zheng XQ, Liu YG et al (2012) Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 42(5):1619–1625PubMedCrossRefGoogle Scholar
  48. 48.
    Gu Q, Ding YS, Jiang XY, Zhang TL (2010) Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection. Amino Acids 38(4):975–983PubMedCrossRefGoogle Scholar
  49. 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
  50. 50.
    Zhang SL, Liang YY (2018) Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J Theor Biol 457(2018):163–169PubMedCrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • Xiaoli Ruan
    • 1
  • Dongming Zhou
    • 1
    Email author
  • Rencan Nie
    • 1
  • Ruichao Hou
    • 1
  • Zicheng Cao
    • 2
  1. 1.Information CollegeYunnan UniversityKunmingChina
  2. 2.School of Public HealthSun Yat-sen UniversityShenzhenChina

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