Protein–protein Interaction Networks and Protein-ligand Docking – Contemporary Insights and Future Perspectives
Keywords:
in silico; protein–protein interaction; protein-ligand docking; molecular modeling
Abstract
Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data, or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry – protein-ligand docking and the prediction of protein-protein interaction networks.
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2. Cicaloni V, Trezza A, Pettini F, Spiga O. Applications of in silico methods for design and development of drugs targeting protein-protein interactions. Curr Top Med Chem 2019;19(7):534-544.
3. Shoemaker BA, Panchenko AR. Deciphering protein-protein interactions. Part I. Experimental techniques and databases. Plos Comput Biol 2007;3(3):e42.
4. Shoemaker BA, Panchenko AR. Deciphering protein-protein interactions. Part II. Experimental techniques and databases. Computational methods to predict protein and domain interaction partners. Plos Comput Biol 2007;3(4):e43.
5. Orchard S, Kerrien S, Abbani S, et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat Methods 2012;9(4):345-350.
6. Zahiri J, Bozorgmehr JH, Masoudi-Nejad A. Computational Prediction of Protein–Protein Interaction Networks: Algorithms and Resources. Curr Genomics 2013;14(6):397-414.
7. Mercatelli D, Scalambra L, Triboli L, et al. Gene regulatory network inference resources: A practical overview. BBA-Gene Regul Mech 2020;1863(6):Article number 194430
8. Li X, Li W, Zeng M, et al. Network-based methods for predicting essential genes or proteins: A survey. Brief Bioinform 2020;21(2):566-583.
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11. Valencia A, Pazos F. Computational methods for the prediction of protein interactions. Curr Opin Struc Biol 2001;12(3):368-373.
12. Skrabanek L, Saini HK, Bader GD, Enright AJ. Computational prediction of protein-protein interactions. Mol Biotechnol 2008;38(1):1-17.
13. Enright A, Iliopoulos I, Kyrpides N, Ouzounis C. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999;402(6757):86-90.
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15. Hue M, Riffle M, Vert JP, Noble WS. Large-scale prediction of protein-protein interactions from structures. BMC Bioinformatics 2010;11(1):144.
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27. Chatterjee P, Basu S, Kundu M, et al. PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables. Cell Mol Biol Lett 2011;16(2):264-278.
28. Zhang M, Su Q, Lu Y, et al. Application of machine learning approaches for protein-protein interactions prediction. Med Chem 2017;13(6):506-514.
29. Zhang L, Yu G, Xia D, Wang J. Protein–protein interactions prediction based on ensemble deep neural networks. Neurocomputing 2019;324:10-19.
30. Li H, Gong X-J, Yu H, Zhou C. Deep neural network based predictions of protein interactions using primary sequences. Molecules 2018;23(8): Article number 1923
31. Lin X, Chen X-W. Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction. Proteomics 2013;13(2):261-268.
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33. Marini S, Xu Q, Yang Q. In silico protein-protein interaction prediction with sequence alignment and classifier stacking. Curr Protein Pept Sc 2011;12(7):614-620.
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Published
2025/12/21
Section
Pregledni rad / Review article