Abstract
The type III secreted effectors (T3SEs) are virulence proteins that play an important role in the pathogenesis of Gram-negative bacteria. They are injected into the host cells by the pathogens, interfere with the immune system of the host cells, and help the growth and reproduction of the pathogens. It is a very challenging task to identify T3SEs because of the high diversity of their sequences and the lack of defined secretion signals. Moreover, their working mechanisms have not been fully understood yet. In order to speed up the recognition of T3SEs and the studies of type III secretion systems, computational tools for the prediction of T3SEs are in great demand. In this study, we regard the protein sequences as a special language. Inspired by the word2vec model in natural language processing, we convert the sequences into word embedding vectors in a similar manner with a specific segmentation strategy for protein sequences. And then we construct the T3SE predictor based on the new sequence feature representation. We conduct experiments on both mono-species data and multi-species data. The experimental results show that the new feature representation model has a competitive performance and can work together with the traditional features to enhance the identification of T3SEs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Galán, J.E., Wolf-Wat, H.: Protein delivery into eukaryotic cells by type III secretion machines. Nature 444(7119), 567 (2006)
Galán, J.E.: Common themes in the design and function of bacterial effectors. Cell Host Microbe 5(6), 571–579 (2009)
He, S.Y., Nomura, K., Whittam, T.S.: Type III protein secretion mechanism in mammalian and plant pathogens. Biochimica et Biophysica Acta (BBA)-Mol. Cell Res. 1694(1–3), 181–206 (2004)
Cornelis, G.R.: The type III secretion injectisome. Nat. Rev. Microbiol. 4(11), 811 (2006)
Yang, Y., Zhao, J., Morgan, R.L., Ma, W., Jiang, T.: Computational prediction of type III secreted proteins from gram-negative bacteria. BMC Bioinform. 11(1), S47 (2010)
Tay, D., Govindarajan, K.R., Khan, A.M., Ong, T., Samad, H.M., Soh, W., Tong, M., Zhang, F., Tan, T.W.: T3SEdb: data warehousing of virulence effectors secreted by the bacterial type III secretion system. BMC Bioinform. 11(S–7), S4 (2010)
Wang, Y., Huang, H., Sun, M., Zhang, Q., Guo, D.: T3DB: an integrated database for bacterial type III secretion system. BMC Bioinform. 13(1), 66 (2012)
Guttman, D.S., McHardy, A.C., Schulze-Lefert, P.: Microbial genome-enabled insights into plant-microorganism interactions. Nat. Rev. Genet. 15(12), 797 (2014)
McDermott, J.E., Corrigan, A., Peterson, E., Oehmen, C., Niemann, G., Cambronne, E.D., Sharp, D., Adkins, J.N., Samudrala, R., Heffron, F.: Computational prediction of type III and IV secreted effectors in gram-negative bacteria. Infect. Immun. 79(1), 23–32 (2011)
Huang, D.-S., Zhao, X.-M., Huang, G.-B., Cheung, Y.-M.: Classifying protein sequences using hydropathy blocks. Pattern Recogn. 39(12), 2293–2300 (2006)
Zhao, X.-M., Du, J.-X., Wang, H.-Q., Zhu, Y., Li, Y.: A new technique for selecting features from protein sequences. Int. J. Pattern Recognit Artif. Intell. 20(02), 271–283 (2006)
Zhao, X.-M., Cheung, Y.-M., Huang, D.-S.: A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Netw. 18(8), 1019–1028 (2005)
Yang, Y., Qi, S.: A new feature selection method for computational prediction of type III secreted effectors. Int. J. Data Min. Bioinform. 10(4), 440–454 (2014)
Löwer, M., Schneider, G.: Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS ONE 4(6), e5917 (2009)
Wang, Y., Zhang, Q., Sun, M., Guo, D.: High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles. Bioinformatics 27(6), 777–784 (2011)
Goldberg, T., Rost, B., Bromberg, Y.: Computational prediction shines light on type III secretion origins. Sci. Rep. 6, 34516 (2016)
Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (2001)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Klein-Seetharaman, J., Reddy, R.: Biological language modeling: convergence of computational linguistics and biological chemistry. Converg. Technol. Improv. Hum. Perform. 378 (2002)
Asgari, E., Mofrad, M.R.K.: Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10(11), e0141287 (2015)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. CoRR, abs/1310.4546 (2013)
Jehl, M.-A., Arnold, R., Rattei, T.: Effective—a database of predicted secreted bacterial proteins. Nucleic Acids Res. 39(Suppl\(\_1\)), D591–D595 (2010)
Dong, X., Lu, X., Zhang, Z.: Bean 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors. Database 2015, bav064 (2015)
Li, W., Godzik, A.: CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13), 1658–1659 (2006)
Song, D., Yang, Y., Yu, B., Zheng, B., Deng, Z., Lu, B.-L., Chen, X., Jiang, T.: Computational prediction of novel non-coding RNAs in Arabidopsis thaliana. BMC Bioinform. 10(1), S36 (2009)
Zhao, X.-M., Wang, Y., Chen, L., Aihara, K.: Gene function prediction using labeled and unlabeled data. BMC Bioinform. 9(1), 57 (2008)
Zhao, X.-M., Li, X., Chen, L., Aihara, K.: Protein classification with imbalanced data. Proteins: Struct. Funct. Bioinform. 70(4), 1125–1132 (2008)
Cheng, B.Y.M., Carbonell, J.G., Klein-Seetharaman, J.: Protein classification based on text document classification techniques. Proteins: Struct. Funct. Bioinform. 58(4), 955–970 (2005)
Dong, Q., Zhou, S., Guan, J.: A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinform. 25(20), 2655–2662 (2009)
Guo, Y., Yu, L., Wen, Z., Li, M.: Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res. 36(9), 3025–3030 (2008)
Liu, B., Wang, X., Lin, L., Dong, Q., Wang, X.: A discriminative method for protein remote homology detection and fold recognition combining top-n-grams and latent semantic analysis. BMC Bioinform. 9(1), 510 (2008)
Chou, K.-C.: Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: Struct. Funct. Bioinform. 43(3), 246–255 (2001)
Chou, K.-C.: Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21(1), 10–19 (2004)
Du, P., Gu, S., Jiao, Y.: PseAAC-general: fast building various modes of general form of chous pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci. 15(3), 3495–3506 (2014)
Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., Chou, K.-C.: Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 43(W1), W65–W71 (2015)
Acknowledgement
This work has been supported by the Shanghai Municipal Natural Science Foundation (No. 16ZR1448700).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Fu, X., Xiao, Y., Yang, Y. (2018). Prediction of Type III Secreted Effectors Based on Word Embeddings for Protein Sequences. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-94968-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94967-3
Online ISBN: 978-3-319-94968-0
eBook Packages: Computer ScienceComputer Science (R0)