IN SILICO STUDY OF APTAMER SPECIFICITY FOR DETECTION OF ADENOSINE TRIPHOSPHATE (ATP) AS BIOSENSOR DEVELOPMENT FOR MITOCHONDRIA DIABETES DIAGNOSIS

Year 2023, , 58 - 69, 15.05.2023

Rustaman Rustaman , Rizky Rafi Rahmawan , Iman Permana Maksum

https://doi.org/10.33435/tcandtc.1181299

Cited By: 1

Abstract

Diabetes Mellitus (DM) is characterized by increased blood glucose levels. It is generally caused by the pancreas' inability to produce insulin due to cell damage or insulin resistance. Due to the inhibition of adenosine triphosphate (ATP) production, which is essential for insulin secretion, one clinical pathology of this complication is insulin secretion dysfunction. Common methods of blood sugar diagnostics cannot distinguish mitochondrial diabetes and can lead to medication errors. Furthermore, an approach was developed through ATP biomarkers using an electrochemical biosensor with the help of an aptamer. However, it remains unknown precisely how and where the molecular interactions between the modified aptamer and ATP occur. Simulations were conducted in this study for 100 ns in silico using the amber18 computer program to determine the stability of the interaction and specificity between aptamer-ATP were compared to ADP and AMP. The results showed that the significant interactions are three hydrogen bonds between ATP and G7, G8, and A24. It was discovered that the aptamer-ATP complex had moderately good interaction and better potential for specificity than ADP and AMP. According to the RMSD, RMSF, and binding energy profiles, the system is still searching for the best conformation, necessitating a longer simulation time and additional studies to optimize the system. As a result, the system can reach a stable state and determine a more accurate energy calculation, hence, it is interpreted according to real applications.

Keywords

aptamer, atp, diabetes mellitus, in silico

Thanks

The author is grateful to the Directorate of Research and Community Service (DRPM) Unpad for the RDPD grant (Research Data Library and Online) No. 1318/UN6.3.1/PT.00/2022 and the 2020 Academic Leadership Grant (ALG) [1427/UN6.3.1/LT/2020 for funding this research.

References

• [1] American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes care. 2011; 34 Suppl 1(Suppl 1), S62–S69.
• [2] Kwak, S. H., Park, K. S., Lee, K. U., & Lee, H. K. Mitochondrial metabolism and diabetes. Journal of diabetes investigation. 2010;1(5), 161–169.
• [3] Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K.. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker insights. 2016; 11, 95–104.
• [4] Huang, Y., Lei, J., Cheng, Y., and Ju, H. Target assistant Zn2+-dependent DNAzyme for signal-on electrochemiluminescent biosensing, Electrochim. Acta. 2015;155, 341–347.
• [5] Frazier, A.E., Thorburn, D.R., and Compton, A.G. Mitochondrial energy generation disorders: Genes, mechanisms, and clues to pathology, J. Biol. Chem. 2019;294 (14).
• [6] Maksum, I.P., Farhani, A., Rachman, S.D., and Ngili, Y. Making of the A3243G mutant template through site directed mutagenesis as positive control in PASA-Mismatch three bases, Int. J. PharmTech Res. 2013;5 (2), 441–450.
• [7] Maksum, I.P., Natradisastra, G., Nuswantara, S., and Ngili, Y. The effect of A3243G mutation of mitochondrial DNA to the clinical features of type-2 diabetes mellitus and cataract, Eur. J. Sci. Res. 2013;96 (4), 591–599.
• [8] Hartati, Y.W., Nur Topkaya, S., Maksum, I.P., and Ozsoz, M. Sensitive detection of mitochondrial DNA A3243G tRNALeu mutation via an electrochemical biosensor using Meldola's Blue as a hybridization indicator, Adv. Anal. Chem. 2013;3 (A), 20–27.
• [9] Destiarani, W., Mulyani, R., Yusuf, M., and Maksum, I.P. Molecular dynamics simulation of T10609C and C10676G mutations of mitochondrial ND4L gene associated with proton translocation in type 2 diabetes mellitus and cataract patients, Bioinf. Biol. Insights. 2020;14, 117793222097867.
• [10] Maksum, I.P., Saputra, S.R., Indrayati, N., Yusuf, M., and Subroto, T. Bioinformatics study of m.9053G>A mutation at the ATP6 gene in relation to type 2 diabetes mellitus and cataract diseases, Bioinf. Biol. Insights. 2017;11, 1177932217728515.
• [11] Maksum, I.P., Maulana, A.F., Yusuf, M., Mulyani, R., Destiarani, W., and Rustaman R. Molecular Dynamics Simulation of a tRNA-Leucine Dimer with an A3243G Heteroplasmy Mutation in Human Mitochondria Using a Secondary Structure Prediction Approach. Indonesia J. Chem. 2022;22 (4), 1043-1051.
• [12] Khlyntseva, S. V., Y. R. Bazel, A. B. Vishnikin, V. Andruch. Methods for the determination of adenosine triphosphate and other adenine nucleotides, Anal. Chem. 2009;64 657–673.
• [13] Huang, Y. F., H. T. Chang. Analysis of adenosine triphosphate and glutathione through gold nanoparticles assisted laser desorption/ionization mass spectrometry. Anal. Chem. 2007;79 4852–4859.
• [14] Ning, Y, Wei K., Cheng L. J., Hu J., Xiang Q. Fluorometric aptamer based determination of adenosine triphosphate based on deoxyribonuclease I-aided target recyclingand signal amplification using graphene oxide as a quencher. Microchim Acta. 2017;184:1847–1854.
• [15] Liu, XJ, Lin BX, Yu Y, Cao YJ, Guo ML. A multifunctional probe based on the use of labeled aptamer and magnetic nanoparticles for fluorometric determination of adenosine 5′-triphosphate. Microchim Acta. 2018;185:243.
• [16] Qu, F., Sun, C., Lv, X. X., You, J. M. A terbium-based metal-organic framework @gold nanoparticle system as a fluorometric probe for aptamer based determination of adenosine triphosphate. Microchim Acta. 2018;185:359.
• [17] Srivastava, Priyanka., S.S. Razi, R. Ali, S. Srivastav, S. Patnaik, S. Srikrishna, A. Misra. Highly sensitive cellimaging "Off-On" fluorescentprobe for mitochondria and ATP. Biosens. Bioelectron. 2015;69 179–185.
• [18] Chen, J.R., X.X. Jiao, H.Q. Luo, N.B. Li. Probe-label-free electrochemical aptasensor based on methylene blue-anchored graphene oxide amplification, J. Mater. Chem. 2013;B1 861–864.
• [19] Yi, Q. and W. Yu. Nanoporous gold particles modified titanium electrode for hydrazine oxidation. J. Electroanal. Chem. 2009;633. 159-164.
• [20] Villalonga, A., Pérez-Calabuig, A. M., & Villalonga, R. Electrochemical biosensors based on nucleic acid aptamer. Analytical and Bioanalytical Chemistry. 2020.
• [21] Kashefi-Kheyrabadi, Leila and Masoud A. Mehrgard. Aptamer based electrochemical biosensor for detection of adenosine triphos-phate using a nanoporous gold platform. Bioelectrochemistry. 2013.
• [22] Nutiu, R., & Li, Y. Structure-Switching Signaling Aptamers. Journal of the American Chemical Society. 2003;123 (16). 4771–4778.
• [23] Odeh, F., Nsairat, H., Alshaer, W., Ismail, M. A., Esawi, E., Qaqish, B., … Ismail, S. I. Aptamer Chemistry: Chemical Modifications and Conjugation Strategies. Molecules. 2019;25(1), 3.
• [24] Huizenga, David and Jack W. Szostak. A DNA Aptamer That Binds Adenosine and ATP. Biochemistry. 1995;34, 656—665.
• [25] Ma, C., Lin, C., Wang, Y., & Chen, X. DNA-based ATP sensing. Trends in Analytical Chemistry. 2016;77. 226-241.
• [26] Wang, Junmei & Cieplak, Piotr & Kollman, Peter. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules?. Journal of Computational Chemistry. 2000;21. 1049-1074.
• [27] Pérez, A., Marchán, I., Svozil, D., Sponer, J., Cheatham, T. E., 3rd, Laughton, C. A., & Orozco, M. Refinement of the AMBER force field for nucleic acids: improving the description of alpha/gamma conformers . Biophysical journal. 2007;92(11).
• [28] Zgarbova, M.; Sponer, J.; Otyepka, M.; Cheatham III, T. E.; Galindo-Murillo, R.; Jurecka, P. Refinement of the Sugar–Phosphate Backbone Torsion Beta for Amber Force Fields Improves the Description of Z-and B-DNA. J. Chem. Theory Comput. 2015; 11, 5723–5736.
• [29] Romelia Salomon-Ferrer; Andreas W. Goetz; Duncan Poole; Scott Le Grand; Ross C. Walker "Routine microsecond molecular dynamics simulations with AMBER - Part II: Particle Mesh Ewald", J. Chem. Theory Comput. 2013.
• [30] Andreas W. Goetz; Mark J. Williamson; Dong Xu; Duncan Poole; Scott Le Grand; Ross C. Walker "Routine microsecond molecular dynamics simulations with AMBER - Part I: Generalized Born", J. Chem. Theory Comput. 2012;8 (5), pp1542-1555.
• [31] Scott Le Grand; Andreas W. Goetz; Ross C. Walker "SPFP: Speed without compromise - a mixed precision model for GPU accelerated molecular dynamics simulations.", Comp. Phys. Comm. 2013; 184 pp374-380.
• [32] Daniel R. Roe and Thomas E. Cheatham, III, "PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data." J. Chem. Theory Comput. 2013;9 (7), pp 3084-3095.