Mreža protein–protein interakcija i protein-ligand doking – trenutno stanje i perspektive
Ključne reči:
in silico; protein–protein interakcije; protein-ligand doking; molekularno modelovanje
Sažetak
Tradiconalna istraživanja bazirana na in vivo and in vitro modelima se dosledno koriste za testiranje biohemijskih hipoteza. U poslednjoj deceniji sve se više razvijaju i računarske (in silico) metodi za razvoj i testiranje hipoteza o biohemijiskim istraživanjima. In silico imaju za cilj da analiziraju kvantitativne aspekte naučnih (velikih) podataka koji se ili čuvaju u velikim bazama podataka ili generišu sofisticiranim alatima za modeliranje i simulaciju; da steknu osnovno razumevanje različitih biohemijskih procesa koji se naročito odnose na velike biološke makromolekule primenom računarskih metoda na velikim skupovima podataka i računanjem ponašanja bioloških sistema. Računarske metode koje se koriste u biohemijskim istraživanjima uključuju mapiranje interakcije proteina i DNK na čitavom genomu, bioinformatiku zasnovanu na proteomici i mapiranje mreža interakcija protein-protein sa visokim propusnim opsegom. Neke od široko korišćenih tehnika molekularnog modeliranja i simulacija su molekularna dinamika, Monte Carlo i Langevinova (stohastička, Brovnijeva) dinamika, kontinuirana elektrostatika, statistička termodinamika, tehnike modeliranja proteina, vezivanje proteina-liganda, izračunavanje afiniteta proteina i liganda i računarska simulacija procesa sakupljanja proteina i delovanje enzima. Ovaj rad predstavlja kratak pregled dve važne metode koje se koriste u studijama biohemije - predviđanje mreža interakcija protein - protein i vezivanje proteina - liganda.
Reference
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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.
9. Koutsoukas A, Simms B, Kirchmair J, et al. From in silico target prediction to multi-target drug design: Current databases, methods and applications. J Proteomics 2011;74(12):2554-2574.
10. Juan D, Pazos F, Valencia A. High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc Natl Acad Sci USA 2008;105(3):934-949.
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.
14. Aloy P, Russell RB. Interrogating protein interaction networks through structural biology. Proc Natl Acad Sci USA 2002;99(9):5896-901.
15. Hue M, Riffle M, Vert JP, Noble WS. Large-scale prediction of protein-protein interactions from structures. BMC Bioinformatics 2010;11(1):144.
16. Li M, Lu Y, Wang J, et al. A topology potential-based method for identifying essential proteins from PPI networks. IEEE ACM T Comput Bi 2015;12(2):372-383.
17. Lei X, Yang X, Fujita H. Random walk based method to identify essential proteins by integrating network topology and biological characteristics. Knowl-Based Syst 2019;167:53-67.
18. Luo J, Kuang L. A new method for predicting essential proteins based on dynamic network topology and complex information. Comput Biol Chem 2104;52:e34-e42.
19. Goldberg DS, Roth FP. Assessing experimentally derived interactions in a small world. Proc Natl Acad Sci USA 2003;100(8):4372-6.
20. Tikk D, Thomas P, Palaga P, et al. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature. PLoS Comput Biol 2010;6(7):Article number e1000837
21. Bui Q-C, Katrenko S, Sloot PMA. A hybrid approach to extract protein-protein interactions. Bioinformatics 2011;27(2):Article number btq620
22. He M, Wang Y, Li W. PPI finder: A mining tool for human protein-protein interactions. PLoS ONE 2009;4(2):Article number e4554
23. Jaeger S, Gaudan S, Leser U, Rebholz-Schuhmann D. Integrating protein-protein interactions and text mining for protein function prediction. BMC Bioinformatics 2008;9(Suppl 8):S2.
24. Shen J, et al. Predicting protein–protein interactions based only on sequences information. Proc Natl Acad Sci USA 2007;104(11):4337-41.
25. Ben Hur A, Ong CS, Sonnenburg S, et al. Support Vector Machines and Kernels for Computational Biology. PLoS Comput Biol 2008;4(10):e1000173.
26. Rashid M, Ramasamy S, PS Raghava G. A simple approach for predicting protein-protein interactions. Curr Pro Pept Sci 2010;11(7):589-600.
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.
32. Thuy Phan TT, Ohkawa T. Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features. BMC Bioinformatics 2016;17:Article number 246
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.
34. Jia J, Xiao X, Liu B. Prediction of Protein–Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests. JALA-J Lab Autom 2016;21(3):368-377.
35. Liu W, Guo Y, Luo J, et al. Prediction of kinase-specific phosphorylational interactions using random forest. Chemometr Intell Lab 2013;126:117-122.
36. Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002;47(4):409-443.
37. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004;3(11):935-949.
38. Autor 2015.
39. Gohlke H, Klebe G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 2002;41(15):2644-2676.
40. Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biolmol Struct 2003;32:335-373.
41. Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking. J Med Chem 2006;49(11):3315-3321.
42. Autor 2019.
43. Autor 2019.
44. Autor 2018.
45. Autor 2018.
46. Autor 2020.
47. Autor 2020.
Objavljeno
2025/12/21
Broj časopisa
Rubrika
Pregledni rad / Review article