Abstract
Background
Protein-protein interactions are essential to many biological processes. The binding site information of protein-protein complexes is extremely useful to obtain their structures from biochemical experiments. Geometric description of protein structures is the precondition of protein binding site prediction and protein-protein interaction analysis. The previous description of protein surface residues is incomplete, and little attention are paid to the implication of residue types for binding site prediction.
Methods
Here, we found three new geometric features to characterize protein surface residues which are very effective for protein-protein interface residue prediction. The new features and several commonly used descriptors were employed to train millions of residue type-nonspecific or specific protein binding site predictors.
Results
The amino acid type-specific predictors are superior to the models without distinction of amino acid types. The performances of the best predictors are much better than those of the sophisticated methods developed before.
Conclusions
The results demonstrate that the geometric properties and amino acid types are very likely to determine if a protein surface residue would become an interface one when the protein binds to its partner.
Article PDF
Similar content being viewed by others
References
Gao, M. and Skolnick, J. (2010) Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected. Proc. Natl. Acad. Sci. USA, 107, 22517–22522
Chothia, C. and Janin, J. (1975) Principles of protein-protein recognition. Nature, 256, 705–708
Jones, S. and Thornton, J. M. (1996) Principles of protein-protein interactions. Proc. Natl. Acad. Sci. USA, 93, 13–20
Keskin, O., Gursoy, A., Ma, B. and Nussinov, R. (2008) Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chem. Rev., 108, 1225–1244
Koshland, D. E. (1995) The key-lock theroy and the induced fit theroy. Angew. Chem. Int. Ed., 33, 2375–2378
Teichmann, S. A. (2002) Principles of protein-protein interactions. Bioinformatics, 18, S249
Zhang, Q. C., Petrey, D., Norel, R. and Honig, B. H. (2010) Protein interface conservation across structure space. Proc. Natl. Acad. Sci. USA, 107, 10896–10901
Aumentado-Armstrong, T. T., Istrate, B. and Murgita, R. A. (2015) Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol. Biol., 10, 7
Esmaielbeiki, R., Krawczyk, K., Knapp, B., Nebel, J. C. and Deane, C. M. (2016) Progress and challenges in predicting protein interfaces. Brief. Bioinformatics, 17, 117–131
Maheshwari, S. and Brylinski, M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief. Bioinform., 16, 1025–1034
Xue, L. C., Dobbs, D., Bonvin, A. M. and Honavar, V. (2015) Computational prediction of protein interfaces: a review of data driven methods. FEBS Lett., 589, 3516–3526
Pintar, A., Carugo, O. and Pongor, S. (2002) CX, an algorithm that identifies protruding atoms in proteins. Bioinformatics, 18, 980–984
de Moraes, F. R., Neshich, I. A., Mazoni, I., Yano, I. H., Pereira, J. G., Salim, J. A., Jardine, J. G. and Neshich, G. (2014) Improving predictions of protein-protein interfaces by combining amino acidspecific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages. PLoS One, 9, e87107
Qin, S. and Zhou, H. X. (2007) meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics, 23, 3386–3387
Segura, J., Jones, P. F. and Fernandez-Fuentes, N. (2011) Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams. BMC Bioinformatics, 12, 352
Zhang, Q. C., Deng, L., Fisher, M., Guan, J., Honig, B. and Petrey, D. (2011) PredUs: a web server for predicting protein interfaces using structural neighbors. Nucleic Acids Res., 39, W283–W287
Wang, L., Wang, Y. and Chang, Q. (2016) Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods, 111, 21–31
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jimenez-Garcia, B., Bates, P. A., Fernandez-Recio, J., Bonvin, A. M. and Weng, Z. (2015) Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427, 3031–3041
Hwang, H., Vreven, T., Janin, J. and Weng, Z. (2010) Proteinprotein docking benchmark version 4.0. Proteins, 78, 3111–3114
Hwang, H., Pierce, B., Mintseris, J., Janin, J. and Weng, Z. (2008) Protein-protein docking benchmark version 3.0. Proteins, 73, 705–709
Hubbard, S.J. and Thornton, M. (1993) Naccess Version 2.1.1. Department of Biochemistry and Molecular Biology, University College, London
Fischer, T. B., Holmes, J. B., Miller, I. R., Parsons, J. R., Tung, L., Hu, J. C. and Tsai, J. (2006) Assessing methods for identifying pair-wise atomic contacts across binding interfaces. J. Struct. Biol., 153, 103–112
Eisenberg, D. (1984) Three-dimensional structure of membrane and surface proteins. Annu. Rev. Biochem., 53, 595–623
Kyte, J. and Doolittle, R. F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 157, 105–132
Olsson, M. H., Søndergaard, C. R., Rostkowski, M. and Jensen, J. H. (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput., 7, 525–537
Møller, M. F. (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw., 6, 525–533
Kishore, R. and Kaur, M. T. (2012) Backpropagation algorithm: an artificial neural network approach for pattern recognition. Inter. J. Sci. & Engin. Res., 3, 1–4
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagating errors. Nature, 323, 533–536
Acknowledgements
Experiments run on Renda Xing Cloud that currently has 64 physical nodes. This research was supported by the National Natural Science Fundation of China (Nos. 31670725 and 91730301), and the State Key Laboratory of Membrane Biology to Xinqi Gong.
Author information
Authors and Affiliations
Corresponding author
Additional information
Author summary: Subtle geometry and chemistry play important roles for protein-protein interactions. The amino acid contact, void and exposure are subtle while significant. Amino acid type additionally determines the behavior when they interacts. We built an integrated approach taking advantage of these features to obtain better protein-protein interface prediction.
Rights and permissions
About this article
Cite this article
Yang, Y., Wang, W., Lou, Y. et al. Geometric and amino acid type determinants for protein-protein interaction interfaces. Quant Biol 6, 163–174 (2018). https://doi.org/10.1007/s40484-018-0138-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40484-018-0138-5