Aptamers Which Target Proteins: What Proteotronics Suggests to Pharmaceutics

Author(s): Rosella Cataldo, Giorgio De Nunzio*, Jean-Francois Millithaler, Eleonora Alfinito

Journal Name: Current Pharmaceutical Design

Volume 26 , Issue 3 , 2020


Become EABM
Become Reviewer
Call for Editor

Abstract:

Aptamers represent a challenging field of research, relevant for diagnosis in macular degeneration, cancer, thrombosis and many inflammatory diseases, and promising in drug discovery and development. Their selection is currently performed by a stable in vitro technology, namely, SELEX. Furthermore, computationalstatistical tools have been developed to complement the SELEX selection; they work both in the preliminary stage of selection, by designing high affinity aptamers for the assigned target, and also in the final stage, analyzing the features of the best performers to implement the selection technique further. A massive use of the in silico approach is, at present, only restricted by the limited knowledge of the specific aptamer-target topology. Actually, only about fifty X-ray structures of aptamer-protein complexes have been experimentally resolved, highlighting how this knowledge has to be improved. The structure of biomolecules like aptamer-protein complexes can be represented by networks, from which several parameters can be extracted. This work briefly reviews the literature, discussing if and how general network parameters in the framework of Proteotronics and graph theory (such as electrical features, link number, free energy change, and assortativity), are important in characterizing the complexes, anticipating some features of the biomolecules.

To better explain this topic, a case-study is proposed, constituted by a set of anti-angiopoietin (Ang2) aptamers, whose performances are known from the experiments, and for which two different types of conformers were predicted. A topological indicator is proposed, named Möbius (M), which combines local and global information, and seems able to discriminate between the two possible types of conformers, so that it can be considered as a useful complement to the in vitro screening for pharmaceutical aims.

Keywords: Aptamers, proteotronics, graph theory, effective affinity, thrombosis, SELEX.

[1]
Bjerregaard N, Andreasen PA, Dupont DM. Expected and unexpected features of protein-binding RNA aptamers. Wiley Interdiscip Rev RNA 2016; 7(6): 744-57.
[http://dx.doi.org/10.1002/wrna.1360] [PMID: 27173731]
[2]
Mirian M, Khanahmad H, Darzi L, Salehi M, Sadeghi-Aliabadi H. Oligonucleotide aptamers: potential novel molecules against viral hepatitis. Res Pharm Sci 2017; 12(2): 88-98.
[http://dx.doi.org/10.4103/1735-5362.202447] [PMID: 28515761]
[3]
Kaur H, Bruno JG, Kumar A, Sharma TK. Aptamers in the Therapeutics and diagnostics pipelines. Theranostics 2018; 8(15): 4016-32.
[http://dx.doi.org/10.7150/thno.25958] [PMID: 30128033]
[4]
Famulok M, Mayer G. Aptamers and SELEX in chemistry & biology. Chem Biol 2014; 21(9): 1055-8.
[http://dx.doi.org/10.1016/j.chembiol.2014.08.003] [PMID: 25237853]
[5]
Koshland DE Jr. The key-lock theory and the induced fit theory. Angew Chem Int Ed Engl 1995; 33(23-4): 2375-8.
[http://dx.doi.org/10.1002/anie.199423751]
[6]
Kinghorn AB, Fraser LA, Lang S, Shiu SCC, Tanner JA. Aptamer Bioinformatics. Int J Mol Sci 2017; 18(12): 2516.
[http://dx.doi.org/10.3390/ijms18122516] [PMID: 29186809]
[7]
Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res 2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[8]
Burley SK, Berman HM, Christie C, et al. RCSB Protein Data Bank: sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Sci 2018; 27(1): 316-30.
[http://dx.doi.org/10.1002/pro.3331] [PMID: 29067736]
[9]
Sakamoto T. NMR study of aptamers. Aptamers 2017; 1: 13-8.
[10]
Davlieva M, Donarski J, Wang J, Shamoo Y, Nikonowicz EP. Structure analysis of free and bound states of an RNA aptamer against ribosomal protein S8 from Bacillus anthracis. Nucleic Acids Res 2014; 42(16): 10795-808.
[http://dx.doi.org/10.1093/nar/gku743] [PMID: 25140011]
[11]
Tuerk C, Gold L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 1990; 249(4968): 505-10.
[http://dx.doi.org/10.1126/science.2200121] [PMID: 2200121]
[12]
Cataldo R, Leuzzi M, Alfinito E. Modelling and development of electrical aptasensors: a short review. Chemosensors (Basel) 2018; 6(20): 1-14.
[http://dx.doi.org/10.3390/chemosensors6020020]
[13]
Gelinas AD, Davies DR, Janjic N. Embracing proteins: structural themes in aptamer-protein complexes. Curr Opin Struct Biol 2016; 36: 122-32.
[http://dx.doi.org/10.1016/j.sbi.2016.01.009] [PMID: 26919170]
[14]
Ali MH, Elsherbiny ME, Emara M. Updates on aptamer research. Int J Mol Sci 2019; 20(10): 2511.
[http://dx.doi.org/10.3390/ijms20102511] [PMID: 31117311]
[15]
Wang QL, Cui HF, Du JF, Lv QJ, Song X. In silico post-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen. RSC Advances 2019; 9: 6328.
[http://dx.doi.org/10.1039/C8RA10163A]
[16]
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2): 455-61.
[PMID: 19499576]
[17]
Kaufmann A, Butcher P, Maden K, Walker S, Widmer M. Using in silico fragmentation to improve routine residue screening in complex matrices. J Am Soc Mass Spectrom 2017; 28(12): 2705-15.
[http://dx.doi.org/10.1007/s13361-017-1800-2] [PMID: 28900836]
[18]
Cataldo R, Ciriaco F, Alfinito E. A validation strategy for in silico generated aptamers. Comput Biol Chem 2018; 77: 123-30.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.09.014] [PMID: 30308477]
[19]
Alfinito E, Pousset J, Reggiani L. Proteotronics: development of protein-based electronics. New York: Pan Stanford Publishing Pte Ltd. 2015.
[http://dx.doi.org/10.1201/b18966]
[20]
Hu WP, Kumar JV, Huang CJ, Chen WY. Computational selection of RNA aptamer against angiopoietin-2 and experimental evaluation. BioMed Res Int 2015; 2015 658712
[http://dx.doi.org/10.1155/2015/658712] [PMID: 25866800]
[21]
Cataldo R, Giotta L, Guascito MR, Alfinito E. Assessing the quality of in silico produced biomolecules: the discovery of a new conformer. J Phys Chem B 2019; 123(6): 1265-73.
[http://dx.doi.org/10.1021/acs.jpcb.8b11456] [PMID: 30642170]
[22]
Okamoto Y, Kokubo H, Tanaka T. Ligand docking simulations by generalized-ensemble algorithms. Adv Protein Chem Struct Biol 2013; 92: 63-91.
[http://dx.doi.org/10.1016/B978-0-12-411636-8.00002-X] [PMID: 23954099]
[23]
Cataldo R, Alfinito E, Reggiani L. Hierarchy and assortativity as new tools for binding-affinity investigation: the case of the TBA aptamer-ligand complex. IEEE Trans Nanobioscience 2017; 16(8): 896-904.
[http://dx.doi.org/10.1109/TNB.2017.2783440] [PMID: 29364133]
[24]
Alfinito E, Reggiani L, Cataldo R, De Nunzio G, Giotta L, Guascito MR. Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors. Nanotechnology 2017; 28(6) 065502
[http://dx.doi.org/10.1088/1361-6528/aa510f] [PMID: 28050975]
[25]
Alfinito E, Reggiani L. Role of topology in electrical properties of bacterio-rhodopsin and rat olfactory receptor I7. Phys Rev E Stat Nonlin Soft Matter Phys 2010; 81(3 Pt 1) 032902
[http://dx.doi.org/10.1103/PhysRevE.81.032902] [PMID: 20365799]
[26]
Alfinito E, Reggiani L. Modeling current-voltage characteristics of proteorhodopsin and bacteriorhodopsin: towards an optoelectronics based on proteins. IEEE Trans Nanobioscience 2016; 15(7): 775-80.
[http://dx.doi.org/10.1109/TNB.2016.2617678] [PMID: 27775530]
[27]
Alfinito E, Pousset J, Reggiani L, Lee K. Photoreceptors for a light biotransducer: a comparative study of the electrical responses of two (type-1) opsins. Nanotechnology 2013; 24(39) 395501
[http://dx.doi.org/10.1088/0957-4484/24/39/395501] [PMID: 24013479]
[28]
Alfinito E, Millithaler JF, Reggiani L. Gumbel distribution and current fluctuations in critical systems. Fluct Noise Lett 2012; 11(03) 1242005
[http://dx.doi.org/10.1142/S0219477512420059]
[29]
Alfinito E, Millithaler JF, Reggiani L, Zine N, Jaffrezic-Renault N. Human olfactory receptor 17-40 as an active part of a nanobiosensor: a microscopic investigation of its electrical properties. RSC Advances 2011; 1(1): 123-7.
[http://dx.doi.org/10.1039/c1ra00025j]
[30]
Alfinito E, Pennetta C, Reggiani L. A network model to correlate conformational change and the impedance spectrum of single proteins. Nanotechnology 2008; 19(6) 065202
[http://dx.doi.org/10.1088/0957-4484/19/6/065202] [PMID: 21730695]
[31]
Fagiani E, Christofori G. Angiopoietins in angiogenesis. Cancer Lett 2013; 328(1): 18-26.
[http://dx.doi.org/10.1016/j.canlet.2012.08.018] [PMID: 22922303]
[32]
White RR, Shan S, Rusconi CP, et al. Inhibition of rat corneal angiogenesis by a nuclease-resistant RNA aptamer specific for angiopoietin-2. Proc Natl Acad Sci USA 2003; 100(9): 5028-33.
[http://dx.doi.org/10.1073/pnas.0831159100] [PMID: 12692304]
[33]
Barton WA, Tzvetkova D, Nikolov DB. Structure of the angiopoietin-2 receptor binding domain and identification of surfaces involved in Tie2 recognition. Structure 2005; 13(5): 825-32.
[http://dx.doi.org/10.1016/j.str.2005.03.009] [PMID: 15893672]
[34]
Boniecki MJ, Lach G, Dawson WK, et al. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res 2016; 44(7) e63
[http://dx.doi.org/10.1093/nar/gkv1479] [PMID: 26687716]
[35]
Van Mieghem P. Performance analysis of complex networks and systems. Cambridge University Press 2014.
[http://dx.doi.org/10.1017/CBO9781107415874]
[36]
Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature 1998; 393(6684): 440-2.
[http://dx.doi.org/10.1038/30918] [PMID: 9623998]
[37]
Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys 2002; 74(1): 47.
[http://dx.doi.org/10.1103/RevModPhys.74.47]
[38]
Di Paola L, De Ruvo M, Paci P, Santoni D, Giuliani A. Protein contact networks: an emerging paradigm in chemistry. Chem Rev 2013; 113(3): 1598-613.
[http://dx.doi.org/10.1021/cr3002356] [PMID: 23186336]
[39]
Piraveenan MR. Topological analysis of complex networks using assortativity. University of Sydney 2010.
[40]
Vishveshwara S, Brinda KV, Kannan N. Protein structure: insights from graph theory. J Theor Comput Chem 2002; 1(01): 187-211.
[http://dx.doi.org/10.1142/S0219633602000117]
[41]
Rosenblatt M. Remarks on some nonparametric estimates of a density function. Ann Math Stat 1956; 27(3): 832-7.
[http://dx.doi.org/10.1214/aoms/1177728190]
[42]
Hintze JL, Nelson RD. Violin plots: a box plot-density trace synergism. Am Stat 1998; 52(2): 181-4.
[43]
Golub GH, Van Loan CF. Matrix Computations. 4th. The Johns Hopkins University Press 2013. P7.3.5


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 26
ISSUE: 3
Year: 2020
Published on: 18 March, 2020
Page: [363 - 371]
Pages: 9
DOI: 10.2174/1381612826666200114095027
Price: $65

Article Metrics

PDF: 26
HTML: 5
EPUB: 1
PRC: 1