A Comprehensive Study on Protein–Protein Interaction in Drug Development: Future Prospects and Challenges
Abstract
Protein–protein interactions (PPIs) play important roles in various cellular processes and have become a major area in drug development. Most of the studies that are related to protein–protein interactions (PPIs) are correlated with different types of diseases, including infectious diseases, cancer, and neurodegenerative disorders. Therefore, targeting PPIs constitutes a therapeutic avenue for disease and an essential strategy for new drug development. In the last 10 years, PPI has been an emerging field in the study of drug intervention, and it’s the most difficult task for drug discovery. In recent years, most PPI modulators have not only been developed, but clinical trials have also been carried out on them for their potential to treat a particular disease. Those that have been approved from an industrial point of view have been suggested by the experts, as they have a broad future in drug discovery. In this study, we focus on summarizing recent advances in the field of PPI, including computational analysis, its types, detection methods, andfuture perspectives for the design of type new drugs targeting PPIs in the future.
Keywords: Protein–protein interactions, computational analysis, detection methods, future design, drug development
INTRODUCTION
Proteins are organic molecules and the building blocks of living organisms, composed of amino acids. Protein–protein interactions (PPIs) play an important role in various biological processes and are often identified as biomarkers in diseases. Targeting PPIs has gained significant attention in drug development [1]. PPIs are involved in many cellular pathways and are often altered in disease states.
Keyworde: Protein–protein interactions, computational analysis, detection methods, future design, drug development
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Refrences:
1. Skwarczynska M, Ottmann C. Protein-protein interactions as drug targets. Future Med Chem.
2015;7(16):2195-2219. doi:10.4155/fmc.15.138.
2. White AW, Westwell AD, Brahemi G. Protein-protein interactions as targets for small-molecule
therapeutics in cancer. Expert Rev Mol Med. 2008;10:e8. doi:10.1017/S1462399408000641.
3. Llères D, Swift S, Lamond AI. Detecting protein-protein interactions in vivo with FRET using
multiphoton fluorescence lifetime imaging microscopy (FLIM). Curr Protocol Cytom.
2007;42(1):12.10.1-12.10.19. doi:10.1002/0471142956.cy1210s42.
4. Nooren IMA, Thornton JM. Diversity of protein–protein interactions. EMBO J. 2003;22(14):3486-
3492. doi:10.1093/emboj/cdg359.
5. Rao VS, Srinivas K, Sujini GN, Kumar GNS. Protein-protein interaction detection: methods and
analysis. Int J Proteomics. 2014;2014:1-12. doi:10.1155/2014/147648.
6. Arkin MR, Randal M, DeLano WL, Hyde J, Luong T, Oslob JD, Raphae DR, Taylor L, Wang J,
McDowell RS, Wells JA, Braisted AC. Binding of small molecules to an adaptive protein–protein
interface. Proc Natl Acad Sci USA. 2003;100(4):1603-1608. doi:10.1073/pnas.252756299.
7. Ozbabacan SEA, Engin HB, Gursoy A, Keskin O. Transient protein-protein interactions. Protein
Eng Des Sel. 2011;24(9):635-648. doi:10.1093/protein/gzr025.
8. Snider J, Kotlyar M, Saraon P, Yao Z, Jurisica I, Stagljar I. Fundamentals of protein interaction
network mapping. Mol Syst Biol. 2015;11(12):848. doi:10.15252/msb.20156351.
9. Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, Seraphin B. A generic protein purification
method for protein complex characterization and proteome exploration. Nat Biotechnol.
1999;17(10):1030-1032. doi:10.1038/13732.
10. Pitre S, Alamgir M, Green JR, Dumontier M, Dehne F, Golshani A. Computational methods for
predicting protein-protein interactions. In: Werther M, Seitz H, editors. Protein–protein interaction.
Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer;
2008;110:247-267. doi:10.1007/10_2007_089.
11. Rohila JS, Chen M, Cerny R, Fromm ME. Improved tandem affinity purification tag and methods
for isolation of protein heterocomplexes from plants. Plant J. 2004;38(1):172-181.
doi:10.1111/j.1365-313X.2004.02031.x.
12. MacBeath G, Schreiber SL. Printing proteins as microarrays for high-throughput function
determination. Sci. 2000;289(5485):1760-1763. doi:10.1126/science.289.5485.1760.
13. Gavin AC, Bösche M, Krause R, Grandi P, Marzioch M, Bauer A, et al. Functional organization of
the yeast proteome by systematic analysis of protein complexes. Nature. 2002;415(6868):141-147.
doi:10.1038/415141a.
14. Tong AHY, Evangelista M, Parsons AB, Xu H, Bader GD, Page N, Robinson M, Raghibizadeh S,
Hogue CWV, Bussey H, Andrews B, Tyers M, Boone C. Systematic genetic analysis with ordered
arrays of yeast deletion mutants. Sci. 2001;294(5550):2364-2368. doi:10.1126/science.1065810.
15. Clackson T, Wells JA. A hot spot of binding energy in a hormone-receptor interface. Sci.
1995;267(5196):383-386. doi:10.1126/science.7529940.
16. Michnick SW, Ear PH, Landry C, Malleshaiah MK, Messier V. Protein-fragment complementation
assays for large-scale analysis, functional dissection, and dynamic studies of protein-protein
interactions in living cells. In: Luttrell LM, Ferguson SSG, editors. Signal Transduction Protocols.
Methods in Molecular Biology. Totowa, New Jersey: Humana Press; 2011;756:395-425.
doi:10.1007/978-1-61779-160-4_25.
17. Casari G, Sander C, Valencia A. A method to predict functional residue in proteins. Nat Struct Biol.
1995;2:171-178. doi:10.1038/nsb0295-171.
18. Uetz P, Glot L, Cagney G, Mansfield TA, Judson RS, Knight JR. A comprehensive analysis of
protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000;403(6770):623-627.
doi:10.1038/35001009.
19. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis
to explore the yeast protein interactome. Proc Natl Acad Sci USA. 2001;98(8):4569-4574.
doi:10.1073/pnas.061034498.
20. Zhang A. Protein interaction networks-computational analysis. New York, USA: Cambridge
University Press; 2009.
21. Zotenko E, Mestre J, O’Leary DP, Przytycka TM. Why do hubs in the yeast protein interaction
network tend to be essential: reexamining the connection between the network topology and
essentiality. PLoS Comput Biol. 2008;4(8):e1000140. doi:10.1371/journal.pcbi.1000140.
22. Pazos F, Valencia A. In silico two-hybrid system for the selection of physically interacting protein
pairs. Proteins: Struct Funct Bioinform. 2002;47(2):219-227. doi:10.1002/prot.10074.
23. Kuzmanov U, Emili A. Protein-protein interaction networks: probing disease mechanisms using
model systems. Genome Med. 2013;5(4):1-12. doi:10.1186/gm441.
24. Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein Data Bank (wwPDB):
ensuring a single, uniform archive of PDB data. Nucleic Acids Res. 2007;35(1):D301-D303.
doi:10.1093/nar/gkl971.
25. Hosur R, Xu J, Bienkowska J, Berger B. IWRAP: an interface threading approach with application
to prediction of cancer-related protein-protein interactions. J Mol Biol. 2011;405(5):1295-1310.
doi:10.1016/j.jmb.2010.11.025.
26. Hosur R, Peng J, Vinayagam A, Stelzl U, Xu J, Perrimon N, Bienkowska J, Berger B. A
computational framework for boosting confidence in high-throughput protein-protein interaction
datasets. Genome Biol. 2012;13(8):R76. doi:10.1186/gb-2012-13-8-r76.
27. Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter
T, Maniatis T, Califano A, Honig B. Structure-based prediction of protein-protein interaction on
genome widescale. Nature. 2012;490(7421):556-560. doi:10.1038/nature11503.
28. Nguyen TP, Ho TB. An integrative domain-based approach to predicting protein-protein
interactions. J Bioinform Comput Biol. 2008;6(6):1115-1132. doi:10.1142/S0219720008003874.
29. Wagner A. How the global structure of protein interaction networks evolves. Proc Royal Soc B.
2003;270(1514):457-466. doi:10.1098/rspb.2002.2269.
30. Du L, Grigsby SM, Yao A, Chang Y,Johnson G, Sun H, Coleska ZN. Peptidomimetics for targeting
protein–protein interactions between DOT1L and MLL oncofusion proteins AF9 and ENL. ACS
Med Chem Lett. 2018;9(9):895-900. doi:10.1021/acsmedchemlett.8b00175.
31. Feng Y, Wang Q, Wang T. Drug target protein-protein interaction networks: a systematic
perspective. Biomed Res Int. 2017;2017:1-13. doi:10.1155/2017/1289259.
32. Haberman AB. Advances in the discovery of protein-protein interaction modulators. London, UK:
SCRIP Insights Informa; 2012.
33. Tong AHY, Drees B, Nardelli G, Bader GD, Brannetti B, Castagnoli L, Evangelista M, Ferracuti
S, Nelson B, Paoluzi S, Michele Q, Zucconi A, Hogue CWV, Fields S, Boone C, Cesareni G. A
combined experimental and computational strategy to define protein interaction networks for
peptide recognition modules. Sci. 2002;295(5553):321-324. doi:10.1126/science.1064987.
34. Modell AE, Blosser SL, Arora PS. Systematic targeting of protein-protein interactions. Trends
Pharmacol Sci. 2016;37(8):702-713. doi:10.1016/j.tips.2016.05.008.
35. Landon MR, Lancia DR, Yu J, Thiel SC, Vajda S. Identification of hot spots within druggable
binding regions by computational solvent mapping of proteins. J Med Chem. 2007;50(6):1231-
1240. doi:10.1021/jm061134b.
36. Cox AD, Fesik SW, Kimmelman AC, Luo J, Der CJ. Drugging the undruggable RAS: mission
possible? Nat Rev Drug Discov. 2014;13:828-851. doi:10.1038/nrd4389.