Geometric and amino acid type determinants for protein-protein interaction interfaces

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.

Graphical abstract

Keywords

protein-protein interaction / protein-protein complex interface / geometry feature / residue type / binding site

References

[1] 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

[2] Chothia, C. and Janin, J. (1975) Principles of protein-protein recognition. Nature, 256, 705–708

[3] Jones, S. and Thornton, J. M. (1996) Principles of protein-protein interactions. Proc. Natl. Acad. Sci. USA, 93, 13–20

[4] 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

[5] Koshland, D. E. (1995) The key-lock theroy and the induced fit theroy. Angew. Chem. Int. Ed., 33, 2375–2378

[6] Teichmann, S. A. (2002) Principles of protein-protein interactions. Bioinformatics, 18, S249

[7] 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

[8] Aumentado-Armstrong, T. T., Istrate, B. and Murgita, R. A. (2015) Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol. Biol., 10, 7

[9] 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

[10] Maheshwari, S. and Brylinski, M. (2015) Predicting protein interface residues using easily accessible on-line resources. Brief. Bioinform., 16, 1025–1034

[11] 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

[12] Pintar, A., Carugo, O. and Pongor, S. (2002) CX, an algorithm that identifies protruding atoms in proteins. Bioinformatics, 18, 980–984

[13] 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 acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages. PLoS One, 9, e87107

[14] Qin, S. and Zhou, H. X. (2007) meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics, 23, 3386–3387

[15] 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

[16] 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

[17] 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

[18] 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

[19] Hwang, H., Vreven, T., Janin, J. and Weng, Z. (2010) Protein-protein docking benchmark version 4.0. Proteins, 78, 3111–3114

[20] Hwang, H., Pierce, B., Mintseris, J., Janin, J. and Weng, Z. (2008) Protein-protein docking benchmark version 3.0. Proteins, 73, 705–709

[21] Hubbard, S.J. and Thornton, M. (1993) Naccess Version 2.1.1. Department of Biochemistry and Molecular Biology, University College, London

[22] 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

[23] Eisenberg, D. (1984) Three-dimensional structure of membrane and surface proteins. Annu. Rev. Biochem., 53, 595–623

[24] Kyte, J. and Doolittle, R. F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 157, 105–132

[25] Olsson, M. H., Søndergaard, C. R., Rostkowski, M. and Jensen, J. H. (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pK_a predictions. J. Chem. Theory Comput., 7, 525–537

[26] Møller, M. F. (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw., 6, 525–533

[27] 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

[28] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagating errors. Nature, 323, 533–536

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-018-0138-5.

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.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Yongxiao Yang, Wei Wang2, Yuan Lou, Jianxin Yin and Xinqi Gong declare that they have no conflict of interests.ƒThis article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature