Protein secretion plays an important role in bacterial lifestyles. In Gram-negative bacteria, a wide range of proteins are secreted to modulate the interactions of bacteria with their environments and other bacteria via various secretion systems. These proteins are essential for the virulence of bacteria, so it is crucial to study them for the pathogenesis of diseases and the development of drugs. Using amino acid composition (AAC), position-specific scoring matrix (PSSM) and N-terminal signal peptides, two different substitution models are firstly constructed to transform protein sequences into numerical vectors. Then, based on support vector machine (SVM) and the “one to one” algorithm, a hybrid multi-classifier named SecretP v.2.2 is proposed to rapidly and accurately distinguish different types of Gram-negative bacterial secreted proteins. When performed on the same test set for a comparison with other methods, SecretP v.2.2 gets the highest total sensitivity of 93.60%. A public independent dataset is used to further test the power of SecretP v.2.2 for predicting NCSPs, it also yields satisfactory results.
Desvaux, M., Hébraud, M., Talon, R. and Henderson, I.R. (2009) Secretion and Subcellular Localizations of Bacterial Proteins: A Semantic Awareness Issue. Trends in Microbiology, 17, 139-145. https://doi.org/10.1016/j.tim.2009.01.004
Chagnot, C., Zorgani, M.A., Astruc, T. and Desvaux, M. (2013) Proteinaceous Determinants of Surface Colonization in Bacteria: Bacterial Adhesion and Biofilm Formation from a Protein Secretion Perspective. Frontiers in Microbiology, 4, 303. https://doi.org/10.3389/fmicb.2013.00303
Bendtsen, J.D., Kiemer, L., Fausboll, A. and Brunak, S. (2005) Non-Classical Protein Secretion in Bacteria. BMC Microbiology, 5, 58. https://doi.org/10.1186/1471-2180-5-58
Wang, G., Chen, H., Xia, Y., Cui, J., Gu, Z., Song, Y., Chen, Y.Q., Zhang, H. and Chen, W. (2013) How Are the Non-Classically Secreted Bacterial Proteins Released into the Extracellular Milieu? Current Microbiology, 67, 688-695. https://doi.org/10.1007/s00284-013-0422-6
Blocker, A., Komoriya, K. and Aizawa, S. (2003) Type III Secretion Systems and Bacterial Flagella: Insights into Their Function from Structural Similarities. Proceedings of the National Academy of Sciences of the United States of America, 100, 3027-3030. https://doi.org/10.1073/pnas.0535335100
Ding, Z., Atmakuri, K. and Christie, P.J. (2003) The Outs and Ins of Bacterial Type IV Secretion Substrates. Trends in Microbiology, 11, 527-535. https://doi.org/10.1016/j.tim.2003.09.004
Konkel, M.E., Kim, B.J., Rivera-Amill, V. and Garvis, S.G. (1999) Bacterial Secreted Proteins Are Required for the Internalization of Campylobacter Jejuni into Cultured Mammalian Cells. Molecular Microbiology, 32, 691-701. https://doi.org/10.1046/j.1365-2958.1999.01376.x
Buttner, D. and Bonas, U. (2003) Common Infection Strategies of Plant and Animal Pathogenic Bacteria. Current Opinion in Plant Biology, 6, 312-319. https://doi.org/10.1016/S1369-5266(03)00064-5
Mudrak, B. and Kuehn, M.J. (2010) Specificity of the Type II Secretion Systems of Enterotoxigenic Escherichia Coli and Vibrio Cholerae for Heat-Labile Enterotoxin and Cholera Toxin. Journal of Bacteriology, 192, 1902-1911. https://doi.org/10.1128/JB.01542-09
Arnold, R., Brandmaier, S., Kleine, F., Tischler, P., Heinz, E., Behrens, S., Niinikoski, A., Mewes, H.W., Horn, M. and Rattei, T. (2009) Sequence-Based Prediction of Type III Secreted Proteins. PLoS Pathogens, 5, e1000376. https://doi.org/10.1371/journal.ppat.1000376
Yang, Y., Zhao, J., Morgan, R.L., Ma, W. and Jiang, T. (2010) Computational Prediction of Type III Secreted Proteins from Gram-Negative Bacteria. BMC Bioinformatics, 11, S47. https://doi.org/10.1186/1471-2105-11-S1-S47
Wang, Y., Zhang, Q., Sun, M.A. and Guo, D. (2011) High-Accuracy Prediction of Bacterial Type III Secreted Effectors Based on Position-Specific Amino Acid Composition Profiles. Bioinformatics, 27, 777-784. https://doi.org/10.1093/bioinformatics/btr021
Yang, Y. (2012) Identification of Novel Type III Effectors Using Latent Dirichlet Allocation. Computational and Mathematical Methods in Medicine, 2012, Article ID: 696190. https://doi.org/10.1155/2012/696190
Sui, T., Yang, Y. and Wang, X. (2013) Sequence-Based Feature Extraction for Type III Effector Prediction. International Journal of Bioscience, Biochemistry and Bioinformatics, 3, 246-251. https://doi.org/10.7763/IJBBB.2013.V3.206
Yang, X., Guo, Y., Luo, J., Pu, X. and Li, M. (2013) Effective Identification of Gram-Negative Bacterial Type III Secreted Effectors Using Position-Specific Residue Conservation Profiles. PLoS One, 8, e84439. https://doi.org/10.1371/journal.pone.0084439
Yang, Y. and Qi, S. (2014) A New Feature Selection Method for Computational Prediction of Type III Secreted Effectors. International Journal of Data Mining and Bioinformatics, 10, 440-454. https://doi.org/10.1504/IJDMB.2014.064894
McDermott, J.E., Corrigan, A., Peterson, E., Oehmen, C., Niemann, G., Cambronne, E.D., Sharp, D., Adkins, J.N., Samudrala, R. and Heffron, F. (2011) Computational Prediction of Type III and IV Secreted Effectors in Gram-Negative Bacteria. Infection and Immunity, 79, 23-32. https://doi.org/10.1128/IAI.00537-10
Zou, L., Nan, C. and Hu, F. (2013) Accurate Prediction of Bacterial Type IV Secreted Effectors Using Amino Acid Composition and PSSM Profiles. Bioinformatics, 29, 3135-3142. https://doi.org/10.1093/bioinformatics/btt554
Wang, Y., Wei, X., Bao, H. and Liu, S.L. (2014) Prediction of Bacterial Type IV Secreted Effectors by C-Terminal Features. BMC Genomics, 15, 50. https://doi.org/10.1186/1471-2164-15-50
Yu, L., Luo, J., Guo, Y., Li, Y., Pu, X. and Li, M. (2013) In Silico Identification of Gram-Negative Bacterial Secreted Proteins from Primary Sequence. Computers in Biology and Medicine, 43, 1177-1181. https://doi.org/10.1016/j.compbiomed.2013.06.001
Kampenusa, I. and Zikmanis, P. (2008) Distinctive Attributes for Predicted Secondary Structures at Terminal Sequences of Non-Classically Secreted Proteins from Proteobacteria. Central European Journal of Biology, 3, 320-326. https://doi.org/10.2478/s11535-008-0026-5
Restrepo-Montoya, D., Pino, C., Nino, L.F., Patarroyo, M.E. and Patarroyo, M.A. (2011) NClassG+: A Classifier for Non-Classically Secreted Gram-Positive Bacterial Proteins. BMC Bioinformatics, 12, 21. https://doi.org/10.1186/1471-2105-12-21
Luo, J., Yu, L., Guo, Y. and Li, M. (2012) Functional Classification of Secreted Proteins by Position Specific Scoring Matrix and Auto Covariance. Chemometrics & Intelligent Laboratory Systems, 110, 163-167. https://doi.org/10.1016/j.chemolab.2011.11.008
Altschul, S.F. and Koonin, E.V. (1998) Iterated Profile Searches with PSI-BLAST—A Tool for Discovery in Protein Databases. Trends in Biochemical Sciences, 23, 444-447. https://doi.org/10.1016/S0968-0004(98)01298-5
Petersen, T.N., Brunak, S., von Heijne, G. and Nielsen, H. (2011) SignalP 4.0: Discriminating Signal Peptides from Transmembrane Regions. Nature Methods, 8, 785-786. https://doi.org/10.1038/nmeth.1701
Guo, Y., Yu, L., Wen, Z. and Li, M. (2008) Using Support Vector Machine Combined with Auto Covariance to Predict Protein-Protein Interactions from Protein Sequences. Nucleic Acids Research, 36, 3025-3030. https://doi.org/10.1093/nar/gkn159
Yu, L., Guo, Y., Zhang, Z., Li, Y., Li, M., Li, G, Xiong, W. and Zeng, Y. (2010) SecretP: A New Method for Predicting Mammalian Secreted Proteins. Peptides, 31, 574-578. https://doi.org/10.1016/j.peptides.2009.12.026
Yu, L., Guo, Y., Li, Y., Li, G., Li, M., Luo, J., Xiong, W. and Qin, W. (2010) SecretP: Identifying Bacterial Secreted Proteins by Fusing New Features into Chou’s Pseudo-Amino Acid Composition. Journal of Theoretical Biology, 267, 1-6. https://doi.org/10.1016/j.jtbi.2010.08.001
Wu, J., Li, M.L., Yu, L.Z. and Wang, C. (2010) An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition. The Protein Journal, 29, 62-67. https://doi.org/10.1007/s10930-009-9222-z
Chou, K.C. and Shen, H.B. (2007) Recent Progress in Protein Subcellular Location Prediction. Analytical Biochemistry, 370, 1-16. https://doi.org/10.1016/j.ab.2007.07.006
Shen, H.B. and Chou, K.C. (2007) Nuc-PLoc: A New Web-Server for Predicting Protein Subnuclear Localization by Fusing PseAA Composition and PsePSSM. Protein Engineering, Design & Selection, 20, 561-567. https://doi.org/10.1093/protein/gzm057