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Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins

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Book cover Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

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Abstract

Self-interacting proteins (SIPs) play a crucial role in investigation of various biochemical developments. In this work, a novel computational method was proposed for accelerating SIPs validation only using protein sequence. Firstly, the protein sequence was represented as Position-Specific Weight Matrix (PSWM) containing protein evolutionary information. Then, we incorporated the Legendre Moment (LM) and Sparse Principal Component Analysis (SPCA) to extract essential and anti-noise evolutionary feature from the PSWM. Finally, we utilized robust Probabilistic Classification Vector Machine (PCVM) classifier to carry out prediction. In the cross-validated experiment, the proposed method exhibits high accuracy performance with 95.54% accuracy on S.erevisiae dataset, which is a significant improvement compared to several competing SIPs predictors. The empirical test reveal that the proposed method can efficiently extracts salient features from protein sequences and accurately predict potential SIPs.

Y.-B. Wang and Z.-H. You—These authors contributed equally to this work.

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References

  1. Chen, Y., Dokholyan, N.V.: Natural selection against protein aggregation on self-interacting and essential proteins in yeast, fly, and worm. Mol. Biol. Evol. 25(8), 1530–1533 (2008)

    Article  Google Scholar 

  2. Gautier, A., Nakata, E., Lukinavicius, G., Tan, K.T., Johnsson, K.: Selective cross-linking of interacting proteins using self-labeling tags. J. Am. Chem. Soc. 131(49), 17954–17962 (2009)

    Article  Google Scholar 

  3. Franceschini, A., et al.: STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, 808–815 (2012)

    Article  Google Scholar 

  4. Rhodes, D.R., et al.: Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol. 23(8), 951–959 (2005)

    Article  Google Scholar 

  5. You, Z.H., Yu, J.Z., Zhu, L., Li, S., Wen, Z.K.: A MapReduce based parallel SVM for large-scale predicting protein–protein interactions. Neurocomputing 145(18), 37–43 (2014)

    Article  Google Scholar 

  6. Zhu, L., You, Z.H., Huang, D.S.: Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding. Neurocomputing 121(18), 99–107 (2013)

    Article  Google Scholar 

  7. You, Z., Li, S., Gao, X., Luo, X., Ji, Z.: Large-scale protein-protein interactions detection by integrating big biosensing data with computational model. Biomed. Res. Int. 2014, 598129 (2014)

    Article  Google Scholar 

  8. You, Z., et al.: Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines. Biomed. Res. Int. 2015, 867516 (2015)

    Article  Google Scholar 

  9. Wang, Y.B., et al.: Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol. BioSyst. 13(7), 1336–1344 (2017)

    Article  Google Scholar 

  10. Wang, L., et al.: Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. J. Theor. Biol. 418(4), 105–110 (2017)

    Article  MathSciNet  Google Scholar 

  11. Li, Z.W., You, Z.H., Chen, X., Gui, J., Nie, R.: Highly accurate prediction of protein-protein interactions via incorporating evolutionary information and physicochemical characteristics. Int. J. Mol. Sci. 17(9), 1396 (2016)

    Article  Google Scholar 

  12. Huang, Y.A., You, Z.H., Xing, C., Yan, G.Y.: Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Syst. Biol. 10(Suppl. 4), 485–494 (2016)

    Google Scholar 

  13. Zhu, L., Deng, S.P., You, Z.H., Huang, D.S.: Identifying spurious interactions in the protein-protein interaction networks using local similarity preserving embedding. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(2), 345–352 (2017)

    Article  Google Scholar 

  14. Huang, Y., You, Z., Li, J., Wong, L., Cai, S.: Predicting protein-protein interactions from amino acid sequences using SaE-ELM combined with continuous wavelet descriptor and PseAA composition. In: International Conference on Intelligent Computing, pp. 634–645 (2015)

    Chapter  Google Scholar 

  15. Li, L., Wang, Y., You, Z., Li, Y., An, J.: PCLPred: a bioinformatics method for predicting protein-protein interactions by combining relevance vector machine model with low-rank matrix approximation. Int. J. Mol. Sci. 19(4), 1029 (2018)

    Article  Google Scholar 

  16. Liu, Z., et al.: Proteome-wide prediction of self-interacting proteins based on multiple properties. Mol. Cell. Proteomics 12(6), 1689–1700 (2013)

    Article  Google Scholar 

  17. Liu, X., Yang, S., Li, C., Zhang, Z., Song, J.: SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information. Amino Acids 48(7), 1655–1665 (2016)

    Article  Google Scholar 

  18. Chen, Z., Li, L., He, Z., Zhou, J., Li, Y., Wong, L.: An improved deep forest model for predicting self-interacting proteins from protein sequence using wavelet transformation. Front. Genet. 10, 90 (2019)

    Article  Google Scholar 

  19. Chen, Z.-H., You, Z.-H., Li, L.-P., Wang, Y.-B., Wong, L., Yi, H.-C.: Prediction of self-interacting proteins from protein sequence information based on random projection model and fast fourier transform. Int. J. Mol. Sci. 20(4), 930 (2019)

    Article  Google Scholar 

  20. Li, J., You, Z., Li, X., Ming, Z., Chen, X.: PSPEL: in silico prediction of self-interacting proteins from amino acids sequences using ensemble learning. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(5), 1165–1172 (2017)

    Article  Google Scholar 

  21. An, J.Y., et al.: Identification of self-interacting proteins by exploring evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix. Oncotarget 7(50), 82440–82449 (2016)

    Article  Google Scholar 

  22. An, J.Y., You, Z.H., Chen, X., Huang, D.S., Yan, G., Wang, D.F.: Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information. Mol. BioSyst. 12(12), 3702 (2016)

    Article  Google Scholar 

  23. Wang, Y., You, Z.: Improving prediction of self-interacting proteins using stacked sparse auto-encoder with PSSM profiles. Int. J. Biol. Sci. 14(8), 983–991 (2018)

    Article  Google Scholar 

  24. Wang, Y., You, Z., Li, X., Jiang, T., Cheng, L., Chen, Z.: Prediction of protein self-interactions using stacked long short-term memory from protein sequences information. BMC Syst. Biol. 12(8), 129 (2018)

    Article  Google Scholar 

  25. Chen, Z.-H., You, Z.-H., Li, L.-P., Wang, Y.-B., Li, X.: RP-FIRF: prediction of self-interacting proteins using random projection classifier combining with finite impulse response filter. In: Huang, D.-S., Jo, K.-H., Zhang, X.-L. (eds.) ICIC 2018. LNCS, vol. 10955, pp. 232–240. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95933-7_29

    Chapter  Google Scholar 

  26. Huang, S.Y., Zou, X.: An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials. J. Comput. Chem. 27(15), 1866–1875 (2006)

    Article  Google Scholar 

  27. You, Z.H., Lei, Y.K., Zhu, L., Xia, J., Wang, B.: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform. 14(S8), S10 (2013)

    Article  Google Scholar 

  28. You, Z., Li, L., Ji, Z., Li, M., Guo, S.: Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor. In: Memetic Computing, pp. 80–85 (2013)

    Google Scholar 

  29. Wang, L., et al.: An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences. Oncotarget 8(3), 5149–5159 (2017)

    Google Scholar 

  30. Yi, H.C., You, Z.H., Huang, D.S., Li, X., Jiang, T.H., Li, L.P.: A deep learning framework for robust and accurate prediction of ncRNA-Protein interactions using evolutionary information. Mol. Ther. Nucleic Acids 11, 337–344 (2018)

    Article  Google Scholar 

  31. Wen, Y., Lei, H., You, Z., Lei, B., Chen, X., Li, L.: Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network. J. Theor. Biol. 430(10), 9–20 (2017)

    Article  Google Scholar 

  32. An, J.Y., Zhang, L., Zhou, Y., Zhao, Y.J., Wang, D.F.: Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information. J. Cheminform. 9(1), 47 (2017)

    Article  Google Scholar 

  33. Wang, Y., You, Z., Li, X., Chen, X., Jiang, T., Zhang, J.: PCVMZM: using the probabilistic classification vector machines model combined with a zernike moments descriptor to predict protein-protein interactions from protein sequences. Int. J. Mol. Sci. 18(5), 1029 (2017)

    Article  Google Scholar 

  34. Wang, Y.-B., You, Z.-H., Li, L.-P., Huang, Y.-A., Yi, H.-C.: Detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in pssm. Molecules 22(8), 1366 (2017)

    Article  Google Scholar 

  35. Wang, Y., et al.: Predicting protein interactions using a deep learning method-stacked sparse autoencoder combined with a probabilistic classification vector machine. Complexity 2018, 12 (2018)

    Google Scholar 

  36. Wang, Y., You, Z.-H., Yang, S., Li, X., Jiang, T.-H., Zhou, X.: A high efficient biological language model for predicting protein-protein interactions. Cells 8(2), 122 (2019)

    Article  Google Scholar 

  37. Zhu, L., You, Z.-H., Huang, D.-S.: Identifying spurious interactions in the protein-protein interaction networks using local similarity preserving embedding. In: Basu, M., Pan, Y., Wang, J. (eds.) ISBRA 2014. LNCS, vol. 8492, pp. 138–148. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08171-7_13

    Chapter  Google Scholar 

  38. You, Z.-H., Zhou, M., Luo, X., Li, S.: Highly efficient framework for predicting interactions between proteins. IEEE Trans. Cybern. 47(3), 731–743 (2017)

    Article  Google Scholar 

  39. Luo, X., Ming, Z., You, Z., Li, S., Xia, Y., Leung, H.: Improving network topology-based protein interactome mapping via collaborative filtering. Knowl.-Based Syst. 90, 23–32 (2015)

    Article  Google Scholar 

  40. Li, Z.W., et al.: Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier. Oncotarget 8(14), 23638 (2017)

    Google Scholar 

  41. An, J.Y., You, Z.H., Meng, F.R., Xu, S.J., Wang, Y.: RVMAB: using the relevance vector machine model combined with average blocks to predict the interactions of proteins from protein sequences. Int. J. Mol. Sci. 17(5), 757 (2016)

    Article  Google Scholar 

  42. You, Z.-H., Lei, Y.-K., Gui, J., Huang, D.-S., Zhou, X.: Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 26(21), 2744–2751 (2010)

    Article  Google Scholar 

  43. You, Z.-H., Huang, W., Zhang, S., Huang, Y.-A., Yu, C.-Q., Li, L.-P.: An efficient ensemble learning approach for predicting protein-protein interactions by integrating protein primary sequence and evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinform. (2018)

    Google Scholar 

  44. You, Z.H., Li, X., Chan, K.C.: An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers: Elsevier Science Publishers B. V. (2017)

    Google Scholar 

  45. You, Z., Ming, Z., Niu, B., Deng, S., Zhu, Z.: A SVM-based system for predicting protein-protein interactions using a novel representation of protein sequences. In: Huang, D.-S., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds.) ICIC 2013. LNCS, vol. 7995, pp. 629–637. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39479-9_73

    Chapter  Google Scholar 

  46. Wong, L., You, Z.-H., Ming, Z., Li, J., Chen, X., Huang, Y.-A.: Detection of interactions between proteins through rotation forest and local phase quantization descriptors. Int. J. Mol. Sci. 17(1), 21 (2015)

    Article  Google Scholar 

  47. Wen, Y.-T., Lei, H.-J., You, Z.-H., Lei, B.-Y., Chen, X., Li, L.-P.: Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network. J. Theor. Biol. 430, 9–20 (2017)

    Article  Google Scholar 

  48. Zhu, L., Deng, S.-P., You, Z.-H., Huang, D.-S.: Identifying spurious interactions in the protein-protein interaction networks using local similarity preserving embedding. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 14(2), 345–352 (2017)

    Article  Google Scholar 

  49. An, J.Y., Meng, F.R., You, Z.H., Fang, Y.H., Zhao, Y.J., Zhang, M.: Using the relevance vector machine model combined with local phase quantization to predict protein-protein interactions from protein sequences. Biomed. Res. Int. 2016(6868), 1–9 (2016)

    Article  Google Scholar 

  50. An, J.Y., Meng, F.R., You, Z.H., Chen, X., Yan, G.Y., Hu, J.P.: Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model. Protein Sci. 25(10), 1825–1833 (2016)

    Article  Google Scholar 

  51. Huang, Y.-A., You, Z.-H., Chen, X., Chan, K., Luo, X.: Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics 17(1), 184 (2016)

    Article  Google Scholar 

  52. Huang, Y.-A., You, Z.-H., Chen, X., Yan, G.-Y.: Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC Syst. Biol. 10(4), 120 (2016)

    Article  Google Scholar 

  53. Huang, Y.-A., et al.: Construction of reliable protein–protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features. Neurocomputing 218, 131–138 (2016)

    Article  Google Scholar 

  54. Lei, Y.-K., You, Z.-H., Dong, T., Jiang, Y.-X., Yang, J.-A.: Increasing reliability of protein interactome by fast manifold embedding. Pattern Recogn. Lett. 34(4), 372–379 (2013)

    Article  Google Scholar 

  55. Lei, Y.-K., You, Z.-H., Ji, Z., Zhu, L., Huang, D.-S.: Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC Bioinformatics 13(Suppl. 7), S3 (2012)

    Article  Google Scholar 

  56. Li, J., Shi, X., You, Z., Chen, Z., Lin, Q., Fang, M.: Using weighted extreme learning machine combined with scale-invariant feature transform to predict protein-protein interactions from protein evolutionary information. In: Huang, D.-S., Bevilacqua, V., Premaratne, P., Gupta, P. (eds.) ICIC 2018. LNCS, vol. 10954, pp. 527–532. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95930-6_49

    Chapter  Google Scholar 

  57. Song, X.-Y., Chen, Z.-H., Sun, X.-Y., You, Z.-H., Li, L.-P., Zhao, Y.: An ensemble classifier with random projection for predicting protein-protein interactions using sequence and evolutionary information. Appl. Sci. 8(1), 89 (2018)

    Article  Google Scholar 

  58. Yi, H.-C., et al.: ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high efficiency feature representation. Mol. Ther. Nucleic Acids 17, 1–9 (2019)

    Article  Google Scholar 

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Acknowledgments

This work is supported in part by the National Science Foundation of China, under Grants 61373086, 11301517 and 61572506. The authors would like to thank all the editors and anonymous reviewers for their constructive advices.

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yan-Bin Wang, Zhu-Hong You, Zhan-Heng Chen & Zhen-Hao Guo

  2. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China

    Hai-cheng Yi

  3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Kai Zheng

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  1. Yan-Bin Wang
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  2. Zhu-Hong You
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  3. Hai-cheng Yi
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  4. Zhan-Heng Chen
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  5. Zhen-Hao Guo
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  6. Kai Zheng
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Contributions

YBW and ZHY considered the algorithm, arranged the datasets, carried out the experiments, HCY and ZHC wrote the manuscript, designed. ZHG and KZ make analyses. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhu-Hong You .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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The publication costs for this article were funded by the corresponding author’s institution.

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Source code of our models and training/testing datasets are available at: https://figshare.com/s/1ff62d10d3bcb94e2bba.

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The authors declare that they have no competing interests.

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Wang, YB., You, ZH., Yi, Hc., Chen, ZH., Guo, ZH., Zheng, K. (2019). Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_44

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