Part:BBa_K5334022
CU2#22 aptamer for Bevacizumab
Notice: Functional DNA
This part is a sequence of a functional ssDNA. It is only active as single-stranded DNA. It can not be cloned into a plasmid. For use order it as a DNA oligo.
Aptamers are single-stranded nucleic acid ligands with high affinity and specificity to target molecules[1]. This is an anti-idiotype aptamer (Figure 1) that recognizes the CDRs of bevacizumab, an anti-vascular endothelial growth factor (VEGF) mAb, which is used in lateral-flow detection system for bevacizumab. The aptasensor allows the detection of bevacizumab in anti-cancer drugs (Avastin) in a wide concentration range, comprised between 0.05 and 5.0 μg/mL, with the limit of detection was 2.09 ng/mL
Usage and Biology
Aptamers, which include RNA, single-stranded DNA (ssDNA), and peptide molecules, exhibit high target specificity and binding strength due to their distinct three-dimensional structures. Research on the binding of nucleic acids with proteins started in the 1980s, largely from studies on HIV and adenovirus, where small structured RNAs were found to bind viral or cellular proteins with precision [6]. For example, the HIV TAR element RNA binds with the viral Tat protein, while adenovirus VA-RNA controls translation [7]. The major breakthrough in aptamer technology came with the 1990 development of SELEX (Systematic Evolution of Ligands by EXponential enrichment), which made it possible to select aptamers in vitro for a range of targets, from small molecules to cells [8].
Since then, aptamers have been explored in fields like diagnostics, therapeutics, biosensors, and drug delivery [8]. They offer advantages over traditional antibodies, such as being highly stable at elevated temperatures, simpler and more cost-effective to produce, and less likely to trigger immune responses [9]. For instance, Macugen, an aptamer targeting VEGF, was approved by the FDA in 2004 for the treatment of wet age-related macular degeneration [9], marking a key milestone for aptamer applications. Given their versatility and superior characteristics, aptamers are seen as a viable alternative to antibodies across various biological and medical fields.
Source of the part
Given the project timeline and efficacy constraints, the SELEX process was deemed infeasible. Consequently, we opted to model an existing aptamer with a demonstrated high affinity for bevacizumab: 5′-GCGGTTGGTGGTAGTTACGTTCGC-3′ [5]. Utilizing the structural ideogram of the previously characterized aptamer [3], we aimed to engineer interactions involving residues that were not previously engaged in antibody binding: T5, T6, G7, G8, T9, G10, G11, T12, A13, G14, T15, T16, A17, C18, G19, T20, T21.
Modeling
Docking
2D Structure
Consequently, we opted to model an existing aptamer with a demonstrated high affinity for bevacizumab: 5′-GCGGTTGGTGGTAGTTACGTTCGC-3′[5]. Utilizing the structural ideogram of the previously characterized aptamer [3], we aimed to engineer interactions involving T21 via changing to G21 (5′-GCGGTTGGTGGTAGTTACGTGCGC-3′ - CU2#22) due to T21 not previously engaged in antibody binding. The DNA secondary structures were predicted using the RNAfold web server [16] with the default parameters (Figure 1). Energy parameters were rescaled to 37 °C and 1.021 M salt concentration. The color represents the base pairing/unpairing probabilities with red being the most plausible.
3D structure
The coordinates of the initial complex (7V5N, 1.70 Å) were taken from the Protein Data Bank [3]. To predict aptamer coordinates 3dDNA web server was used for folding [10] (Figure 2).
For virtual screening, an approach from [11] was adopted. Normalized results of 3 different models (Autodock Vina, PatchDock, and Hex), were used. Smina [12] which is a modification of Autodock vina used for docking as it is capable of setting up the simulation box automatically according to the initial ligand position. The grid box was chosen with 8 Å buffer space in each direction to ensure that different folds would fit. The exhaustiveness was 4000 and 10 best docking poses were taken for further evaluation. Hex 8.0 [13] was used with default parameters and consequent minimization using the OPLS force field. PatchDock [14] docking was performed locally with 4.0 Å RMSD clustering threshold. Therefore, we adopted the methodology outlined by Ahirwar et al. [15]. For binding affinity predictions, we employed three distinct docking platforms: Autodock Vina, PatchDock, and Hex (Table 1), and took a total score as characteristic of binding.
Table 1. | Z-score of aptamers from three different docking platforms (Autodock Vina, PatchDock, and Hex) and their cumulative sum for aptamers A14#1 and CU2#22.
| # | Subsequence | AutoDock Vina | PatchDock | Hex | Sum |
|---|---|---|---|---|---|
| A14#1 | 5′-GCGGTTGGTGGTAGTTACGTTCGC-3′ | -2.224 | -1.280 | -1.814 | -5,318 |
| CU2#22 | 5′-GCGGTTGGTGGTAGTTACGTGCGC-3′ | -1.324 | -1.320 | -1.682 | -4.326 |
Aptamer CU2#22 showed worse results than initial aptamer A14#1. The docking results indicated that the initial aptamer A14#1 exhibits a stronger affinity for bevacizumab compared to aptamer CU2#22. Consequently, for molecular dynamics, lab experiments, and the development of the LFA device, the decision was made to focus on other aptamers.
References
[1] Ellington AD, Szostak JW. In vitro selection of RNA molecules that bind specific ligands. Nature. 1990 Aug 30;346(6287):818-22.
[2]Nonaka, Y., Sode, K., & Ikebukuro, K. (2010). Screening and Improvement of an Anti-VEGF DNA Aptamer. Molecules, 15(1), 215–225.
[3] Saito T, Shimizu Y, Tsukakoshi K, Abe K, Lee J, Ueno K, Asano R, Jones BV, Yamada T, Nakano T, Tong J, Hishiki A, Hara K, Hashimoto H, Sode K, Toyo'oka T, Todoroki K, Ikebukuro K. Development of a DNA aptamer that binds to the complementarity-determining region of therapeutic monoclonal antibody and affinity improvement induced by pH-change for sensitive detection. Biosens Bioelectron. 2022 May 1;203:114027.
[4] Hasegawa, H., Sode, K., & Ikebukuro, K. (2008). Selection of DNA aptamers against VEGF165 using a protein competitor and the aptamer blotting method. Biotechnology Letters, 30(5), 829–834.
[5] Yamada, T., Saito, T., Hill, Y., Shimizu, Y., Tsukakoshi, K., Mizuno, H., … Todoroki, K. (2019). High-throughput bioanalysis of bevacizumab in human plasma based on enzyme-linked aptamer assay using anti-idiotype DNA aptamer. Analytical Chemistry.
[6] Dollins CM, Nair S, Sullenger BA. Aptamers in immunotherapy. Hum Gene Ther. 2008 May;19(5):443-50.
[7] Sullenger BA, Gallardo HF, Ungers GE, Gilboa E. Overexpression of TAR sequences renders cells resistant to human immunodeficiency virus replication. Cell. 1990 Nov 2;63(3):601-8.
[8] Han K, Liang Z, Zhou N. Design strategies for aptamer-based biosensors. Sensors (Basel). 2010;10(5):4541-57.
[9] Mascini M. Aptamers and their applications. Anal Bioanal Chem. 2008 Feb;390(4):987-8. doi: 10.1007/s00216-007-1769-y. PMID: 18193207
[10] Zhang, Y., Xiong, Y. & Xiao, Y. 3dDNA: A Computational Method of Building DNA 3D Structures. Molecules 27, 5936 (2022).
[11] Ahirwar, R. et al. In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules. Sci Rep 6, 21285 (2016).
[12] Koes, D. R., Baumgartner, M. P. & Camacho, C. J. Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise. J Chem Inf Model 53, 1893–1904 (2013).
[13] Ghoorah, A. W., Devignes, M., Smaïl‐Tabbone, M. & Ritchie, D. W. Protein docking using case‐based reasoning. Proteins: Structure, Function, and Bioinformatics 81, 2150–2158 (2013).
[14] Duhovny, D., Nussinov, R. & Wolfson, H. J. Efficient Unbound Docking of Rigid Molecules. in 185–200 (2002).
[15] Ahirwar, R. et al. In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules. Sci Rep 6, 21285 (2016).
[16] Hofacker, I. L. Vienna RNA secondary structure server. Nucleic Acids Res 31, 3429–3431 (2003). Sequence and Features
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