Abstract
The experiment of alanine scanning has shown that most of the binding energies in protein-protein interactions are contributed by a few significant residues at the protein-protein interfaces, and those important residues are called hot spot residues. On the basis of protein-protein interaction, hot spot residues tend to get together to form modules, and those modules are defined as hot regions. So, hot spot residues play an important role in revealing the life activities of organisms. Therefore, how to predict hot spot residues and non-spot residues effectively and accurately is a vital research direction. A new method is proposed combining protein amino acid physicochemical features and structural features to predict the hot spot residues based on the ensemble learning. The experimental results demonstrate that this method of prediction hot spot residues has a good effect.
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References
Hsu, C.M., Chen, C.Y., Liu, B.J., Huang, C.C.: Identification of hot regions in protein-protein interactions by sequential pattern mining. BMC Bioinform. 8(Suppl 5), S8 (2007)
Keskin, O., Tuncbag, N., Gursoy, A.: Predicting protein-protein interactions from the molecular to the proteome level. Chem. Rev. 116(8), 4884–4909 (2016)
Yu, X., Rangwala, H., Domeniconi, G., Zhang, G.J., Yu, Z.W.: Protein function prediction using multilabel ensemble classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(4), 1045–1057 (2013)
Hsu, C.M., Chen, C.Y., Liu, B.J.: MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences. Nucleic Acids Res. 36(4), 1400–1406 (2008)
Scott, D.E., Bayly, A.R., Abell, C., Skidmore, J.: Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat. Rev. Drug Discov. 15, 533–550 (2016)
Sahu, S.S., Panda, G.: Efficient localization of hot spots in proteins using a novel s-transform based filtering approach. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(5), 1235–1246 (2011)
Tuncbag, N., Gursoy, A., Keskin, O.: Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics 25(12), 1513–1520 (2009)
Reichmann, D., Rahat, O., Albeck, S., Meged, R., Dym, O., Schreiber, G.: The modular architecture of protein-protein binding interfaces. Proc. Natl. Acad. Sci. 102(1), 57–62 (2005)
Ahmad, S., Keskin, O., Sarai, A., Nussinov, R.: Protein–DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins. Nucleic Acids Res. 36(18), 5922–5932 (2008)
Armon, A., Dan, G., Ben-Tal, N.: ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. J. Mol. Biol. 307(1), 447–463 (2001)
Keskin, O., Ma, B.Y., Mol, R.J.: Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol. 345(5), 1281–1294 (2005)
Xu, B., Wei, X.M., Deng, L., Guan, J., Zhou, S.G.: A semi-supervised boosting SVM for predicting hot spots at protein-protein interfaces. BMC Syst. Biol. 2(2), 1–12 (2012)
Morrison, K.L., Weiss, G.A.: Combinatorial alanine-scanning. Curr. Opin. Chem. Biol. 5(3), 302–307 (2001)
Thorn, K.S., Bogan, A.A.: ASEdb: a data base of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics 17(3), 284–285 (2001)
Gonzalez Ruiz, D., Gohlke, H.: Targeting protein-protein interactions with small molecules: challenges and perspectives for computational biding epitope detection and ligand finding. Curr. Med. Chem. 13(22), 2607–2625 (2006)
Ezkurdia, I., Bartoli, L., Fariselli, P., Casadio, R., Valencia, A., Tress, M.L.: Progress and challenges in predicting protein-protein interaction sites. Brief. Bioinform. 10(10), 233–246 (2009)
Lise, S., Buchan, D., Pontil, M., Jones, D.T.: Predictions of hot spot residues at protein-protein interfaces using support vector machines. PLoS One 6(2), e16774 (2011). doi:10.1371/journal.pone.0016774
Lise, S., Archambeau, C., Pontil, M., Jones, D.T.: Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. 10(1), 365 (2009). doi:10.1186/1471-2105-10-365
Tuncbag, N., Keskin, O., Gursoy, A.: HotPoint: hot spot prediction server for protein interfaces. Nucleic Acids Res. 38, 402–406 (2010)
Cukuroglu, E., Gursoy, A., Keskin, O.: Analysis of hot region organization in hub proteins. Ann. Biomed. Eng. 38(6), 2068–2078 (2010)
Carles, P., Fabian, G., Juan, F.: Prediction of protein-binding areas by small world residue networks and application to docking. BMC Bioinform. 12, 378–388 (2011)
Cho, K., Kim, D., Lee, D.: A feature-based approach to modeling protein-protein interaction hot spots. Nucleic Acids Res. 37(8), 2672–2687 (2009)
Nan, D.F., Zhang, X.L.: Prediction of hot regions in protein-protein interactions based on complex network and community detection. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 17–23 (2013)
Hu, J., Zhang, X.L., Liu, X.M., Tang, J.S.: Prediction of hot regions in protein-protein interaction by combining density-based in cremental clustering with feature-based classification. Comput. Biol. Med. 61, 127–137 (2015)
Mihel, J., Sikić, M., Tomić, S., Jeren, B., Vlahovicek, K.: PSAIA-protein structure and interaction analyzer. BMC Struct. Biol. 8(1), 1–11 (2008)
Li, Z.R., Lin, H.H., Han, L.Y., et al.: PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res. 34, W32–W37 (2015)
Burgoyne, N., Jackson, R.: Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interface. Bioinformatics 22(11), 1335–1342 (2006)
Li, B.Q., Feng, K.Y., Li, C., Huang, T.: Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS. PLoS ONE 7(8), e43927 (2012)
Lin, X., Zhang, X.: Identification of hot regions in protein-protein interactions based on detecting local community structure. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 432–438. Springer, Cham (2016). doi:10.1007/978-3-319-42291-6_43
Yugandhar, K., Gromiha, M.M.: Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches. Proteins Struct. Funct. Bioinform. 82(9), 2088–2096 (2014)
Lin, X., Zhang, X.: Prediction and analysis of hot region in protein-protein interactions. In: BIBM 2016, pp. 1598–1603 (2016)
Acknowledgment
The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported in part by National Natural Science Foundation of China (No. 61502356, 61273225, 61273303).
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Lin, X., Huang, Q., Zhou, F. (2017). Effective Identification of Hot Spots in PPIs Based on Ensemble Learning. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_18
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DOI: https://doi.org/10.1007/978-3-319-63312-1_18
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