Generic placeholder image

Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

Recent Advances in Computer-aided Virtual Screening and Docking Optimization for Aptamer

Author(s): Yijie Liu, Jie Yang*, Meilun Chen, Xiaoling Lu, Zheng Wei, Chunhua Tang and Peng Yu*

Volume 23, Issue 20, 2023

Published on: 12 July, 2023

Page: [1985 - 2000] Pages: 16

DOI: 10.2174/1568026623666230623145802

Price: $65

Abstract

Aptamers, as artificially synthesized short nucleotide sequences, have been widely used in protein analysis, gene engineering, and molecular diagnostics. Currently, the screening process of aptamers still relies on the traditional SELEX process, which is cumbersome and complex. Moreover, the success rate of aptamer screening through the SELEX process is not high, which has become a major challenge. In recent years, the development of computers has facilitated virtual screening, which can greatly accelerate the screening process of aptamers through computer-assisted screening. However, the accuracy and precision of current virtual screening software on the market vary. Therefore, this work summarizes the docking characteristics of four mainstream molecular docking software programs, including Auto dock, Auto dock Vina, MOE, and hex Dock, in recent years. Moreover, the accuracy and prediction performance of these four molecular docking software programs for aptamer docking based on experimental data is also evaluated. This will guide researchers in the selection of molecular docking software. Additionally, this review provides a detailed overview of the application of computer-aided virtual screening in aptamer screening, thus providing a direction for future development in this field.

« Previous
Graphical Abstract

[1]
Tuerk, C.; Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science, 1990, 249(4968), 505-510.
[http://dx.doi.org/10.1126/science.2200121] [PMID: 2200121]
[2]
Yu, H.; Alkhamis, O.; Canoura, J.; Liu, Y.; Xiao, Y. Advances and challenges in small-molecule DNA aptamer isolation, characterization, and sensor development. Angew. Chem. Int. Ed., 2021, 60(31), 16800-16823.
[http://dx.doi.org/10.1002/anie.202008663] [PMID: 33559947]
[3]
Moutsiopoulou, A.; Broyles, D.; Dikici, E.; Daunert, S.; Deo, S. K. Molecular aptamer beacons and their applications in sensing, imaging, and diagnostics. Small, 2019, 15(35), 1902248.
[http://dx.doi.org/10.1002/smll.201902248]
[4]
Zhu, G.; Chen, X. Aptamer-based targeted therapy. Adv. Drug Deliv. Rev., 2018, 134, 65-78.
[http://dx.doi.org/10.1016/j.addr.2018.08.005] [PMID: 30125604]
[5]
Kinghorn, A.; Fraser, L.; Liang, S.; Shiu, S.; Tanner, J. Aptamer Bioinformatics. Int. J. Mol. Sci., 2017, 18(12), 2516.
[http://dx.doi.org/10.3390/ijms18122516] [PMID: 29186809]
[6]
Canoura, J.; Yu, H.; Alkhamis, O.; Roncancio, D.; Farhana, R.; Xiao, Y. Accelerating post-SELEX aptamer engineering using exonuclease digestion. J. Am. Chem. Soc., 2021, 143(2), 805-816.
[http://dx.doi.org/10.1021/jacs.0c09559] [PMID: 33378616]
[7]
Lee, K.H.; Zeng, H. A general double library SELEX strategy for aptamer selection using unmodified nonimmobilized targets. Anal. Bioanal. Chem., 2017, 409(21), 5081-5089.
[http://dx.doi.org/10.1007/s00216-017-0454-z] [PMID: 28634758]
[8]
Chinnappan, R.; Zaghloul, N.S.; AlZabn, R.; Malkawi, A.; Abdel Rahman, A.; Abu-Salah, K.M.; Zourob, M. Aptamer selection and aptasensor construction for bone density biomarkers. Talanta, 2021, 224, 121818.
[http://dx.doi.org/10.1016/j.talanta.2020.121818] [PMID: 33379043]
[9]
Kissmann, A.K.; Wolf, D.; Krämer, M.; Müller, F.; Amann, V.; Xing, H.; Gottschalk, K.E.; Weil, T.; Eichmann, R.; Schäfer, P.; Rosenau, F. Polyclonal aptamer libraries from a fluroot-selex for the specific labeling of the apical and elongation/differentiation zones of Arabidopsis thaliana roots. Int. J. Mol. Sci., 2022, 23(20), 12220.
[http://dx.doi.org/10.3390/ijms232012220] [PMID: 36293073]
[10]
Xing, H.; Zhang, Y.; Krämer, M.; Kissmann, A.K.; Henkel, M.; Weil, T.; Knippschild, U.; Rosenau, F. A polyclonal selex aptamer library directly allows specific labelling of the human gut bacterium Blautia producta without isolating individual aptamers. Molecules, 2022, 27(17), 5693.
[http://dx.doi.org/10.3390/molecules27175693] [PMID: 36080459]
[11]
Xing, H.; Zhang, Y.; Krämer, M.; Kissmann, A.K.; Amann, V.; Raber, H.F.; Weil, T.; Stieger, K.R.; Knippschild, U.; Henkel, M.; Andersson, J.; Rosenau, F. A polyclonal aptamer library for the specific binding of the gut bacterium Roseburia intestinalis in mixtures with other gut microbiome bacteria and human stool samples. Int. J. Mol. Sci., 2022, 23(14), 7744.
[http://dx.doi.org/10.3390/ijms23147744] [PMID: 35887092]
[12]
Yu, H.; Pan, C.; Zhu, J.; Shen, G.; Deng, Y.; Xie, X.; Geng, X.; Wang, L. Selection and identification of a DNA aptamer for fluorescent detection of netilmicin. Talanta, 2022, 250, 123708.
[http://dx.doi.org/10.1016/j.talanta.2022.123708] [PMID: 35752088]
[13]
Wang, T.; Chen, C.; Larcher, L.M.; Barrero, R.A.; Veedu, R.N. Three decades of nucleic acid aptamer technologies: Lessons learned, progress and opportunities on aptamer development. Biotechnol. Adv., 2019, 37(1), 28-50.
[http://dx.doi.org/10.1016/j.biotechadv.2018.11.001] [PMID: 30408510]
[14]
Yang, J.; Bowser, M.T. Capillary electrophoresis-SELEX selection of catalytic DNA aptamers for a small-molecule porphyrin target. Anal. Chem., 2013, 85(3), 1525-1530.
[http://dx.doi.org/10.1021/ac302721j] [PMID: 23234289]
[15]
Stoltenburg, R.; Reinemann, C.; Strehlitz, B. FluMag-SELEX as an advantageous method for DNA aptamer selection. Anal. Bioanal. Chem., 2005, 383(1), 83-91.
[http://dx.doi.org/10.1007/s00216-005-3388-9] [PMID: 16052344]
[16]
Rabal, O.; Pastor, F.; Villanueva, H.; Soldevilla, M.M.; Hervas-Stubbs, S.; Oyarzabal, J. in silico Aptamer docking studies: From a retrospective validation to a prospective case study’tim3 aptamers binding. Mol. Ther. Nucleic Acids, 2016, 5(10), e376.
[http://dx.doi.org/10.1038/mtna.2016.84] [PMID: 27754489]
[17]
Paniel, N.; Istamboulié, G.; Triki, A.; Lozano, C.; Barthelmebs, L.; Noguer, T. Selection of DNA aptamers against penicillin G using Capture-SELEX for the development of an impedimetric sensor. Talanta, 2017, 162, 232-240.
[http://dx.doi.org/10.1016/j.talanta.2016.09.058] [PMID: 27837823]
[18]
Chen, Z.; Hu, L.; Zhang, B.T.; Lu, A.; Wang, Y.; Yu, Y.; Zhang, G. Artificial intelligence in aptamer–target binding prediction. Int. J. Mol. Sci., 2021, 22(7), 3605.
[http://dx.doi.org/10.3390/ijms22073605] [PMID: 33808496]
[19]
Lu, W.; Zhang, R.; Jiang, H.; Zhang, H.; Luo, C. Computer-aided drug design in epigenetics. Front Chem., 2018, 6, 57.
[http://dx.doi.org/10.3389/fchem.2018.00057] [PMID: 29594101]
[20]
Yusuf, M.; Destiarani, W.; Firdaus, A.R.R.; Rohmatulloh, F.G.; Novianti, M.T.; Pradini, G.W.; Dwiyana, R.F. Residual interactions of LL-37 with POPC and POPE:POPG bilayer model studied by all-atom molecular dynamics simulation. Int. J. Mol. Sci., 2022, 23(21), 13413.
[http://dx.doi.org/10.3390/ijms232113413] [PMID: 36362195]
[21]
Wei, H.; Guo, Z.; Long, Y.; Liu, M.; Xiao, J.; Huang, L.; Yu, Q.; Li, P. Aptamer-based high-throughput screening model for efficient selection and evaluation of natural ingredients against SGIV infection. Viruses, 2022, 14(6), 1242.
[http://dx.doi.org/10.3390/v14061242] [PMID: 35746713]
[22]
Bruno, J.G. Successes and failures of static aptamer-target 3D docking models. Int. J. Mol. Sci., 2022, 23(22), 14410.
[http://dx.doi.org/10.3390/ijms232214410] [PMID: 36430888]
[23]
Bruno, J.G. Integration of multiple computer modeling software programs for characterization of a brain natriuretic peptide sandwich DNA aptamer complex. J. Mol. Recognit., 2019, 32(12), e2809.
[http://dx.doi.org/10.1002/jmr.2809] [PMID: 31418487]
[24]
Yan, C.; Zhang, J.; Yao, L.; Xue, F.; Lu, J.; Li, B.; Chen, W. Aptamer-mediated colorimetric method for rapid and sensitive detection of chloramphenicol in food. Food Chem., 2018, 260, 208-212.
[http://dx.doi.org/10.1016/j.foodchem.2018.04.014] [PMID: 29699664]
[25]
Zhao, Z.; Wang, H.; Zhai, W.; Feng, X.; Fan, X.; Chen, A.; Wang, M. A lateral flow strip based on a truncated aptamer-complementary strand for detection of type-B aflatoxins in nuts and dried figs. Toxins, 2020, 12(2), 136.
[http://dx.doi.org/10.3390/toxins12020136] [PMID: 32098355]
[26]
Liu, R.; Zhang, F.; Sang, Y.; Liu, M.; Shi, M.; Wang, X. Selection and characterization of dna aptamers for constructing aptamer-aunps colorimetric method for detection of AFM1. Foods, 2022, 11(12), 1802.
[http://dx.doi.org/10.3390/foods11121802] [PMID: 35742000]
[27]
Mairal Lerga, T.; Jauset-Rubio, M.; Skouridou, V.; Bashammakh, A.S.; El-Shahawi, M.S.; Alyoubi, A.O.; O’Sullivan, C.K. High affinity aptamer for the detection of the biogenic amine histamine. Anal. Chem., 2019, 91(11), 7104-7111.
[http://dx.doi.org/10.1021/acs.analchem.9b00075] [PMID: 31042376]
[28]
Villa, A.; Brunialti, E.; Dellavedova, J.; Meda, C.; Rebecchi, M.; Conti, M.; Donnici, L.; De Francesco, R.; Reggiani, A.; Lionetti, V.; Ciana, P. DNA aptamers masking angiotensin converting enzyme 2 as an innovative way to treat SARS-CoV-2 pandemic. Pharmacol. Res., 2022, 175, 105982.
[http://dx.doi.org/10.1016/j.phrs.2021.105982] [PMID: 34798263]
[29]
Kong, D.; Yeung, W.; Hili, R. in vitro selection of diversely functionalized aptamers. J. Am. Chem. Soc., 2017, 139(40), 13977-13980.
[http://dx.doi.org/10.1021/jacs.7b07241] [PMID: 28938065]
[30]
Zhao, S.; Tian, R.; Wu, J.; Liu, S.; Wang, Y.; Wen, M.; Shang, Y.; Liu, Q.; Li, Y.; Guo, Y.; Wang, Z.; Wang, T.; Zhao, Y.; Zhao, H.; Cao, H.; Su, Y.; Sun, J.; Jiang, Q.; Ding, B. A DNA origami-based aptamer nanoarray for potent and reversible anticoagulation in hemodialysis. Nat. Commun., 2021, 12(1), 358.
[http://dx.doi.org/10.1038/s41467-020-20638-7] [PMID: 33441565]
[31]
Singh, S.K.; Gordetsky, J.B.; Bae, S.; Acosta, E.P.; Lillard, J.W., Jr; Singh, R. Selective targeting of the hedgehog signaling pathway by pbm nanoparticles in docetaxel-resistant prostate cancer. Cells, 2020, 9(9), 1976.
[http://dx.doi.org/10.3390/cells9091976] [PMID: 32867229]
[32]
Fellows, T.; Ho, L.; Flanagan, S.; Fogel, R.; Ojo, D.; Limson, J. Gold nanoparticle-streptavidin conjugates for rapid and efficient screening of aptamer function in lateral flow sensors using novel CD4-binding aptamers identified through Crossover-SELEX. Analyst, 2020, 145(15), 5180-5193.
[http://dx.doi.org/10.1039/D0AN00634C] [PMID: 32567629]
[33]
Zhou, G.; Da Won Bae, S.; Nguyen, R.; Huo, X.; Han, S.; Zhang, Z.; Hebbard, L.; Duan, W.; Eslam, M.; Liddle, C.; Yuen, L.; Lam, V.; Qiao, L.; George, J. An aptamer-based drug delivery agent (CD133-apt-Dox) selectively and effectively kills liver cancer stem-like cells. Cancer Lett., 2021, 501, 124-132.
[http://dx.doi.org/10.1016/j.canlet.2020.12.022] [PMID: 33352247]
[34]
Li, L.; Wan, J.; Wen, X.; Guo, Q.; Jiang, H.; Wang, J.; Ren, Y.; Wang, K. Identification of a New DNA Aptamer by Tissue-SELEX for Cancer Recognition and Imaging. Anal. Chem., 2021, 93(19), 7369-7377.
[http://dx.doi.org/10.1021/acs.analchem.1c01445] [PMID: 33960774]
[35]
Sun, M.; Liu, S.; Wei, X.; Wan, S.; Huang, M.; Song, T.; Lu, Y.; Weng, X.; Lin, Z.; Chen, H.; Song, Y.; Yang, C. Aptamer blocking strategy inhibits SARS-CoV-2 virus infection. Angew. Chem. Int. Ed., 2021, 60(18), 10266-10272.
[http://dx.doi.org/10.1002/anie.202100225] [PMID: 33561300]
[36]
Park, G.; Lee, M.; Kang, J.; Park, C.; Min, J.; Lee, T. Selection of DNA aptamer and its application as an electrical biosensor for Zika virus detection in human serum. Nano Converg., 2022, 9(1), 41.
[http://dx.doi.org/10.1186/s40580-022-00332-8] [PMID: 36087171]
[37]
Hayashi, T.; Oshima, H.; Mashima, T.; Nagata, T.; Katahira, M.; Kinoshita, M. Binding of an RNA aptamer and a partial peptide of a prion protein: Crucial importance of water entropy in molecular recognition. Nucleic Acids Res., 2014, 42(11), 6861-6875.
[http://dx.doi.org/10.1093/nar/gku382] [PMID: 24803670]
[38]
Tan, S.Y.; Acquah, C.; Sidhu, A.; Ongkudon, C.M.; Yon, L.S.; Danquah, M.K. SELEX modifications and bioanalytical techniques for aptamer–target binding characterization. Crit. Rev. Anal. Chem., 2016, 46(6), 521-537.
[http://dx.doi.org/10.1080/10408347.2016.1157014] [PMID: 26980177]
[39]
Quiroga, R.; Villarreal, M.A. Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening. PLoS One, 2016, 11(5), e0155183.
[http://dx.doi.org/10.1371/journal.pone.0155183] [PMID: 27171006]
[40]
Crampon, K.; Giorkallos, A.; Deldossi, M.; Baud, S.; Steffenel, L.A. Machine-learning methods for ligand–protein molecular docking. Drug Discov. Today, 2022, 27(1), 151-164.
[http://dx.doi.org/10.1016/j.drudis.2021.09.007] [PMID: 34560276]
[41]
Biyani, M.; Yasuda, K.; Isogai, Y.; Okamoto, Y.; Weilin, W.; Kodera, N.; Flechsig, H.; Sakaki, T.; Nakajima, M.; Biyani, M. Novel DNA aptamer for CYP24A1 inhibition with enhanced antiproliferative activity in cancer cells. ACS Appl. Mater. Interfaces, 2022, 14(16), 18064-18078.
[http://dx.doi.org/10.1021/acsami.1c22965] [PMID: 35436103]
[42]
Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H. J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res., 2005, 33, W363-7.
[http://dx.doi.org/10.1093/nar/gki481]
[43]
Jokar, M.; Safaralizadeh, M.H.; Hadizadeh, F.; Rahmani, F.; Kalani, M.R. Apta-nanosensor preparation and in vitro assay for rapid Diazinon detection using a computational molecular approach. J. Biomol. Struct. Dyn., 2017, 35(2), 343-353.
[http://dx.doi.org/10.1080/07391102.2016.1140594] [PMID: 26924613]
[44]
Albada, H.B.; Golub, E.; Willner, I. Computational docking simulations of a DNA-aptamer for argininamide and related ligands. J. Comput. Aided Mol. Des., 2015, 29(7), 643-654.
[http://dx.doi.org/10.1007/s10822-015-9844-5] [PMID: 25877490]
[45]
Zavyalova, E.; Turashev, A.; Novoseltseva, A.; Legatova, V.; Antipova, O.; Savchenko, E.; Balk, S.; Golovin, A.; Pavlova, G.; Kopylov, A. Pyrene-Modified DNA aptamers with high affinity to wild-type EGFR and EGFRvIII. Nucleic Acid Ther., 2020, 30(3), 175-187.
[http://dx.doi.org/10.1089/nat.2019.0830] [PMID: 31990606]
[46]
Zhai, Q.; Gao, C.; Ding, J.; Zhang, Y.; Islam, B.; Lan, W.; Hou, H.; Deng, H.; Li, J.; Hu, Z.; Mohamed, H.I.; Xu, S.; Cao, C.; Haider, S.M.; Wei, D. Selective recognition of c-MYC Pu22 G-quadruplex by a fluorescent probe. Nucleic Acids Res., 2019, 47(5), 2190-2204.
[http://dx.doi.org/10.1093/nar/gkz059] [PMID: 30759259]
[47]
Yang, Y.; Tang, Y.; Wang, C.; Liu, B.; Wu, Y. Selection and identification of a DNA aptamer for ultrasensitive and selective detection of λ-cyhalothrin residue in food. Anal. Chim. Acta, 2021, 1179, 338837.
[http://dx.doi.org/10.1016/j.aca.2021.338837] [PMID: 34535250]
[48]
Oh, I.H.; Park, D.Y.; Cha, J.M.; Shin, W.R.; Kim, J.H.; Kim, S.C.; Cho, B-K.; Ahn, J-Y.; Kim, Y-H. Docking simulation and sandwich assay for aptamer-based botulinum neurotoxin Type C detection. Biosensors, 2020, 10(8), 98.
[http://dx.doi.org/10.3390/bios10080098] [PMID: 32806662]
[49]
Ma, P.; Ye, H.; Guo, H.; Ma, X.; Yue, L.; Wang, Z. Aptamer truncation strategy assisted by molecular docking and sensitive detection of T-2 toxin using SYBR Green I as a signal amplifier. Food Chem., 2022, 381, 132171.
[http://dx.doi.org/10.1016/j.foodchem.2022.132171] [PMID: 35124487]
[50]
Zhu, C.; Li, L.; Fang, S.; Zhao, Y.; Zhao, L.; Yang, G.; Qu, F. Selection and characterization of an ssDNA aptamer against thyroglobulin. Talanta, 2021, 223(Pt 1), 121690.
[http://dx.doi.org/10.1016/j.talanta.2020.121690] [PMID: 33303143]
[51]
Chen, K.; Zhu, L.; Du, Z.; Lan, X.; Huang, K.; Zhang, W.; Xu, W. Docking-aided rational tailoring of a fluorescence- and affinity-enhancing aptamer for a label-free ratiometric malachite green point-of-care aptasensor. J. Hazard. Mater., 2023, 447, 130798.
[http://dx.doi.org/10.1016/j.jhazmat.2023.130798] [PMID: 36669418]
[52]
Hu, B.; Zhou, R.; Li, Z.; Ouyang, S.; Li, Z.; Hu, W.; Wang, L.; Jiao, B. Study of the binding mechanism of aptamer to palytoxin by docking and molecular simulation. Sci. Rep., 2019, 9(1), 15494.
[http://dx.doi.org/10.1038/s41598-019-52066-z] [PMID: 31664144]
[53]
Cosconati, S.; Forli, S.; Perryman, A.L.; Harris, R.; Goodsell, D.S.; Olson, A.J. Virtual screening with AutoDock: Theory and practice. Expert Opin. Drug Discov., 2010, 5(6), 597-607.
[http://dx.doi.org/10.1517/17460441.2010.484460] [PMID: 21532931]
[54]
Ritchie, D.W.; Venkatraman, V. Ultra-fast FFT protein docking on graphics processors. Bioinformatics, 2010, 26(19), 2398-2405.
[http://dx.doi.org/10.1093/bioinformatics/btq444] [PMID: 20685958]
[55]
Torabi, R.; Bagherzadeh, K.; Ghourchian, H.; Amanlou, M. An investigation on the interaction modes of a single-strand DNA aptamer and RBP4 protein: A molecular dynamic simulations approach. Org. Biomol. Chem., 2016, 14(34), 8141-8153.
[http://dx.doi.org/10.1039/C6OB01094F] [PMID: 27511589]
[56]
Khan, N.H.; Bui, A.A.; Xiao, Y.; Sutton, R.B.; Shaw, R.W.; Wylie, B.J.; Latham, M.P. A DNA aptamer reveals an allosteric site for inhibition in metallo-β-lactamases. PLoS One, 2019, 14(4), e0214440.
[http://dx.doi.org/10.1371/journal.pone.0214440] [PMID: 31009467]
[57]
Sun, L.; Fu, T.; Zhao, D.; Fan, H.; Zhong, S. Divide-and-link peptide docking: A fragment-based peptide docking protocol. Phys. Chem. Chem. Phys., 2021, 23(39), 22647-22660.
[http://dx.doi.org/10.1039/D1CP02098F] [PMID: 34596658]
[58]
Gong, Z.; Zhao, Y.; Chen, C.; Xiao, Y. Role of ligand binding in structural organization of add A-riboswitch aptamer: A molecular dynamics simulation. J. Biomol. Struct. Dyn., 2011, 29(2), 403-416.
[http://dx.doi.org/10.1080/07391102.2011.10507394] [PMID: 21875158]
[59]
Poojara, L.; K, R.; Rawal, R.M. Computational approaches screening DNA aptamers against conserved outer membrane protein W of Vibrio cholerae O1- an investigation expanding the potential for point-of-care detection with aptasensors. J. Biomol. Struct. Dyn., 2023, •••, 1-12.
[http://dx.doi.org/10.1080/07391102.2023.2181634] [PMID: 36812260]
[60]
Filipe, H.A.L.; Loura, L.M.S. Molecular Dynamics Simulations: Advances and Applications. Molecules, 2022, 27(7), 2105.
[http://dx.doi.org/10.3390/molecules27072105] [PMID: 35408504]
[61]
Bashir, A.; Yang, Q.; Wang, J.; Hoyer, S.; Chou, W.; McLean, C.; Davis, G.; Gong, Q.; Armstrong, Z.; Jang, J.; Kang, H.; Pawlosky, A.; Scott, A.; Dahl, G.E.; Berndl, M.; Dimon, M.; Ferguson, B.S. Machine learning guided aptamer refinement and discovery. Nat. Commun., 2021, 12(1), 2366.
[http://dx.doi.org/10.1038/s41467-021-22555-9] [PMID: 33888692]
[62]
Di Gioacchino, A.; Procyk, J.; Molari, M.; Schreck, J.S.; Zhou, Y.; Liu, Y.; Monasson, R.; Cocco, S.; Šulc, P. Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection. PLOS Comput. Biol., 2022, 18(9), e1010561.
[http://dx.doi.org/10.1371/journal.pcbi.1010561] [PMID: 36174101]
[63]
Deo, R.C. Machine Learning in Medicine. Circulation, 2015, 132(20), 1920-1930.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.115.001593] [PMID: 26572668]
[64]
Buchan, D.W.A.; Jones, D.T. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res., 2019, 47(W1), W402-W407.
[http://dx.doi.org/10.1093/nar/gkz297] [PMID: 31251384]
[65]
Kelley, D.R.; Snoek, J.; Rinn, J.L. Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 2016, 26(7), 990-999.
[http://dx.doi.org/10.1101/gr.200535.115] [PMID: 27197224]
[66]
Segar, M.W.; Patel, K.V.; Ayers, C.; Basit, M.; Tang, W.H.W.; Willett, D.; Berry, J.; Grodin, J.L.; Pandey, A. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis. Eur. J. Heart Fail., 2020, 22(1), 148-158.
[http://dx.doi.org/10.1002/ejhf.1621] [PMID: 31637815]
[67]
Lai, Y.; Lin, P.; Lin, F.; Chen, M.; Lin, C.; Lin, X.; Wu, L.; Zheng, M.; Chen, J. Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning. Front. Immunol., 2022, 13, 1046410.
[http://dx.doi.org/10.3389/fimmu.2022.1046410] [PMID: 36569892]
[68]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[69]
Chen, Z.; Zhao, P.; Li, C.; Li, F.; Xiang, D.; Chen, Y.Z.; Akutsu, T.; Daly, R.J.; Webb, G.I.; Zhao, Q.; Kurgan, L.; Song, J. iLearnPlus: A comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Res., 2021, 49(10), e60.
[http://dx.doi.org/10.1093/nar/gkab122] [PMID: 33660783]
[70]
Auwul, M.R.; Rahman, M.R.; Gov, E.; Shahjaman, M.; Moni, M.A. Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19. Brief. Bioinform., 2021, 22(5), bbab120.
[http://dx.doi.org/10.1093/bib/bbab120] [PMID: 33839760]
[71]
Zhou, X.; Song, H.; Li, J. Residue-frustration-based prediction of protein–protein interactions using machine learning. J. Phys. Chem. B, 2022, 126(8), 1719-1727.
[http://dx.doi.org/10.1021/acs.jpcb.1c10525] [PMID: 35170967]
[72]
Pandurangan, A. P.; Blundell, T. L. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. Protein Sci., 2020, 29(1), 247-257.
[http://dx.doi.org/10.1002/pro.3774]
[73]
Yap, S. H. K.; Pan, J.; Linh, D. V.; Zhang, X.; Wang, X.; Teo, W. Z.; Zamburg, E.; Tham, C. K.; Yew, W. S.; Poh, C. L. Engineered nucleotide chemicapacitive microsensor array augmented with physics-guided machine learning for high-throughput screening of cannabidiol. Small, 2022, 18(22), e2107659.
[http://dx.doi.org/10.1002/smll.202107659]
[74]
Lee, W.; Han, K. Constructive prediction of potential RNA aptamers for a protein target. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2020, 17(5), 1476-1482.
[http://dx.doi.org/10.1109/TCBB.2019.2951114] [PMID: 31689200]
[75]
Nosrati, M.; amani, J. in silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach. Comput. Biol. Chem., 2021, 95, 107568.
[http://dx.doi.org/10.1016/j.compbiolchem.2021.107568] [PMID: 34543910]
[76]
Wu, N.; Zhang, X.Y.; Xia, J.; Li, X.; Yang, T.; Wang, J.H. Ratiometric 3D DNA machine combined with machine learning algorithm for ultrasensitive and high-precision screening of early urinary diseases. ACS Nano, 2021, 15(12), 19522-19534.
[http://dx.doi.org/10.1021/acsnano.1c06429] [PMID: 34813275]
[77]
Yang, Q.; Jia, C.; Li, T. Prediction of aptamer–protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier. Math. Biosci., 2019, 311, 103-108.
[http://dx.doi.org/10.1016/j.mbs.2019.01.009] [PMID: 30880100]
[78]
Dong, L.; Watson, J.; Cao, S.; Arregui, S.; Saxena, V.; Ketz, J.; Awol, A.K.; Cohen, D.M.; Caterino, J.M.; Hains, D.S.; Schwaderer, A.L. Aptamer based proteomic pilot study reveals a urine signature indicative of pediatric urinary tract infections. PLoS One, 2020, 15(7), e0235328.
[http://dx.doi.org/10.1371/journal.pone.0235328] [PMID: 32628701]
[79]
Torkamanian-Afshar, M.; Nematzadeh, S.; Tabarzad, M.; Najafi, A.; Lanjanian, H.; Masoudi-Nejad, A. in silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm. Mol. Divers., 2021, 25(3), 1395-1407.
[http://dx.doi.org/10.1007/s11030-021-10192-9] [PMID: 33554306]
[80]
Schmidt, C.M.; Smolke, C.D. A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements. eLife, 2021, 10, e59697.
[http://dx.doi.org/10.7554/eLife.59697] [PMID: 33860764]
[81]
Khan, A.; Uddin, J.; Ali, F.; Ahmad, A.; Alghushairy, O.; Banjar, A.; Daud, A. Prediction of antifreeze proteins using machine learning. Sci. Rep., 2022, 12(1), 20672.
[http://dx.doi.org/10.1038/s41598-022-24501-1] [PMID: 36450775]
[82]
Humphreys, I.R.; Pei, J.; Baek, M.; Krishnakumar, A.; Anishchenko, I.; Ovchinnikov, S.; Zhang, J.; Ness, T.J.; Banjade, S.; Bagde, S.R.; Stancheva, V.G.; Li, X.H.; Liu, K.; Zheng, Z.; Barrero, D.J.; Roy, U.; Kuper, J.; Fernández, I.S.; Szakal, B.; Branzei, D.; Rizo, J.; Kisker, C.; Greene, E.C.; Biggins, S.; Keeney, S.; Miller, E.A.; Fromme, J.C.; Hendrickson, T.L.; Cong, Q.; Baker, D. Computed structures of core eukaryotic protein complexes. Science, 2021, 374(6573), eabm4805.
[http://dx.doi.org/10.1126/science.abm4805] [PMID: 34762488]
[83]
Das, S.; Chakrabarti, S. Classification and prediction of protein–protein interaction interface using machine learning algorithm. Sci. Rep., 2021, 11(1), 1761.
[http://dx.doi.org/10.1038/s41598-020-80900-2] [PMID: 33469042]
[84]
Cunningham, J.M.; Koytiger, G.; Sorger, P.K.; AlQuraishi, M. Biophysical prediction of protein–peptide interactions and signaling networks using machine learning. Nat. Methods, 2020, 17(2), 175-183.
[http://dx.doi.org/10.1038/s41592-019-0687-1] [PMID: 31907444]
[85]
Kaundal, R.; Loaiza, C.D.; Duhan, N.; Flann, N. deepHPI: A comprehensive deep learning platform for accurate prediction and visualization of host–pathogen protein–protein interactions. Brief. Bioinform., 2022, 23(3), bbac125.
[http://dx.doi.org/10.1093/bib/bbac125] [PMID: 35511057]
[86]
Zhang, L.; Zhang, C.; Gao, R.; Yang, R.; Song, Q. Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes. BMC Bioinformatics, 2016, 17(1), 225.
[http://dx.doi.org/10.1186/s12859-016-1087-5] [PMID: 27245069]
[87]
Mou, J.; Ding, J.; Qin, W. Deep learning-enhanced potentiometric aptasensing with magneto-controlled sensors. Angew. Chem. Int. Ed., 2023, 62(3), e202210513.
[http://dx.doi.org/10.1002/anie.202210513] [PMID: 36404278]
[88]
Premkumar, K.A.R.; Bharanikumar, R.; Palaniappan, A. Riboflow: Using deep learning to classify riboswitches with ~99% accuracy. Front. Bioeng. Biotechnol., 2020, 8, 808.
[http://dx.doi.org/10.3389/fbioe.2020.00808] [PMID: 32760712]
[89]
Emami, N.; Ferdousi, R. AptaNet as a deep learning approach for aptamer–protein interaction prediction. Sci. Rep., 2021, 11(1), 6074.
[http://dx.doi.org/10.1038/s41598-021-85629-0] [PMID: 33727685]
[90]
Wang, S.; Dong, H.; Shen, W.; Yang, Y.; Li, Z.; Liu, Y.; Wang, C.; Gu, B.; Zhang, L. Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning. RSC Advances, 2021, 11(55), 34425-34431.
[http://dx.doi.org/10.1039/D1RA05778B] [PMID: 35494737]
[91]
Su, Y.; Lin, R.; Wang, J.; Tan, D.; Zheng, C. Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data. Brief. Bioinform., 2023, 24(2), bbad021.
[http://dx.doi.org/10.1093/bib/bbad021] [PMID: 36715275]
[92]
Wang, Y.; Wang, X.; Cui, X.; Meng, J.; Rong, R. Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs. Mol. Ther. Nucleic Acids, 2023, 31, 411-420.
[http://dx.doi.org/10.1016/j.omtn.2023.01.014] [PMID: 36845339]
[93]
Pan, Z.; Zhou, S.; Zou, H.; Liu, C.; Zang, M.; Liu, T.; Wang, Q. CRMSNET : A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPS for RNA sequence. Proteins, 2023, 2023, prot.26489.
[http://dx.doi.org/10.1002/prot.26489] [PMID: 36935548]
[94]
Song, T.; Dai, H.; Wang, S.; Wang, G.; Zhang, X.; Zhang, Y.; Jiao, L. TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer. Front. Genet., 2022, 13, 1038919.
[http://dx.doi.org/10.3389/fgene.2022.1038919] [PMID: 36303549]
[95]
Gupta, S.; Shankar, R. miWords: Transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes. Brief. Bioinform., 2023, 24(2), bbad088.
[http://dx.doi.org/10.1093/bib/bbad088] [PMID: 36907657]
[96]
Song, Y.; Wang, Y.; Wang, X.; Huang, D.; Nguyen, A.; Meng, J. Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes. Brief. Bioinform., 2023, 24(3), bbad105.
[http://dx.doi.org/10.1093/bib/bbad105] [PMID: 36932656]
[97]
Navien, T.N.; Thevendran, R.; Hamdani, H.Y.; Tang, T.H.; Citartan, M. in silico molecular docking in DNA aptamer development. Biochimie, 2021, 180, 54-67.
[http://dx.doi.org/10.1016/j.biochi.2020.10.005] [PMID: 33086095]
[98]
El-Hachem, N.; Haibe-Kains, B.; Khalil, A.; Kobeissy, F.H.; Nemer, G. AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study. Methods Mol. Biol., 2017, 1598, 391-403.
[http://dx.doi.org/10.1007/978-1-4939-6952-4_20] [PMID: 28508374]
[99]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[100]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2009, 31(2), NA.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[101]
Gaillard, T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J. Chem. Inf. Model., 2018, 58(8), 1697-1706.
[http://dx.doi.org/10.1021/acs.jcim.8b00312] [PMID: 29989806]
[102]
Ritchie, D.W.; Kemp, G.J.L. Protein docking using spherical polar Fourier correlations. Proteins, 2000, 39(2), 178-194.
[http://dx.doi.org/10.1002/(SICI)1097-0134(20000501)39:2<178::AID-PROT8>3.0.CO;2-6] [PMID: 10737939]
[103]
Wei, H.; Cai, R.; Yue, H.; Tian, Y.; Zhou, N. Screening and application of a truncated aptamer for high-sensitive fluorescent detection of metronidazole. Anal. Chim. Acta, 2020, 1128, 203-210.
[http://dx.doi.org/10.1016/j.aca.2020.07.003] [PMID: 32825904]
[104]
Eissa, S.; Alkhaldi, S.; Chinnappan, R.; Siddiqua, A.; Abduljabbar, M.; Abdel Rahman, A.M.; Dasouki, M.; Zourob, M. Selection, characterization, and electrochemical biosensing application of DNA aptamers for sepiapterin. Talanta, 2020, 216, 120951.
[http://dx.doi.org/10.1016/j.talanta.2020.120951] [PMID: 32456943]
[105]
Mastronardi, E.; Cyr, K.; Monreal, C.M.; DeRosa, M.C. Selection of DNA aptamers for root exudate L -serine using multiple selection strategies. J. Agric. Food Chem., 2021, 69(14), 4294-4306.
[http://dx.doi.org/10.1021/acs.jafc.0c06796] [PMID: 33600189]
[106]
Chinnappan, R.; Eissa, S.; Alotaibi, A.; Siddiqua, A.; Alsager, O.A.; Zourob, M. in vitro selection of DNA aptamers and their integration in a competitive voltammetric biosensor for azlocillin determination in waste water. Anal. Chim. Acta, 2020, 1101, 149-156.
[http://dx.doi.org/10.1016/j.aca.2019.12.023] [PMID: 32029106]
[107]
Niu, C.; Zhang, C.; Liu, J. Capture-SELEX of DNA aptamers for estradiol specifically and estrogenic compounds collectively. Environ. Sci. Technol., 2022, 56(24), 17702-17711.
[http://dx.doi.org/10.1021/acs.est.2c05808] [PMID: 36441874]
[108]
He, J.; Wang, J.; Zhang, M.; Shi, G. Selection of a structure-switching aptamer for the specific methotrexate detection. ACS Sens., 2021, 6(6), 2436-2441.
[http://dx.doi.org/10.1021/acssensors.1c00749] [PMID: 34132539]
[109]
Torres-Vázquez, B.; de Lucas, A.M.; García-Crespo, C.; García-Martín, J.A.; Fragoso, A.; Fernández-Algar, M.; Perales, C.; Domingo, E.; Moreno, M.; Briones, C. in vitro selection of high affinity DNA and RNA aptamers that detect hepatitis C virus core protein of genotypes 1 to 4 and inhibit virus production in cell culture. J. Mol. Biol., 2022, 434(7), 167501.
[http://dx.doi.org/10.1016/j.jmb.2022.167501] [PMID: 35183559]
[110]
Liu, M.; Wang, J.; Chang, Y.; Zhang, Q.; Chang, D.; Hui, C.Y.; Brennan, J.D.; Li, Y. in vitro selection of a DNA aptamer targeting degraded protein fragments for biosensing. Angew. Chem. Int. Ed., 2020, 59(20), 7706-7710.
[http://dx.doi.org/10.1002/anie.202000025] [PMID: 32155319]
[111]
Thevendran, R.; Tang, T.H.; Citartan, M. In-silico selection employing rigid docking and molecular dynamic simulation in selecting DNA aptamers against androgen receptor. Biotechnol. J., 2023, 18(4), 2200092.
[http://dx.doi.org/10.1002/biot.202200092] [PMID: 36735817]
[112]
Singh, M.; Tripathi, P.; Singh, S.; Sachan, M.; Chander, V.; Sharma, G.K.; De, U.K.; Kota, S.; Putty, K.; Singh, R.K.; Nara, S. Identification and characterization of DNA aptamers specific to VP2 protein of canine parvovirus. Appl. Microbiol. Biotechnol., 2021, 105(23), 8895-8906.
[http://dx.doi.org/10.1007/s00253-021-11651-x] [PMID: 34714365]
[113]
Carrión-Marchante, R.; Frezza, V.; Salgado-Figueroa, A.; Pérez-Morgado, M.I.; Martín, M.E.; González, V.M. DNA Aptamers against Vaccinia-Related Kinase (VRK) 1 Block Proliferation in MCF7 Breast Cancer Cells. Pharmaceuticals, 2021, 14(5), 473.
[http://dx.doi.org/10.3390/ph14050473] [PMID: 34067799]
[114]
Gao, T.; Ding, P.; Li, W.; Wang, Z.; Lin, Q.; Pei, R. Isolation of DNA aptamers targeting N-cadherin and high-efficiency capture of circulating tumor cells by using dual aptamers. Nanoscale, 2020, 12(44), 22574-22585.
[http://dx.doi.org/10.1039/D0NR06180H] [PMID: 33174555]
[115]
Escamilla-Gutiérrez, A.; Córdova-Espinoza, M.G.; Sánchez-Monciváis, A.; Tecuatzi-Cadena, B.; Regalado-García, A.G.; Medina-Quero, K. in silico selection of aptamers for bacterial toxins detection. J. Biomol. Struct. Dyn., 2022, 2022, 1-10.
[http://dx.doi.org/10.1080/07391102.2022.2159529] [PMID: 36546716]
[116]
Oliviero, G.; Stornaiuolo, M.; D’Atri, V.; Nici, F.; Yousif, A.M.; D’Errico, S.; Piccialli, G.; Mayol, L.; Novellino, E.; Marinelli, L.; Grieco, P.; Carotenuto, A.; Noppen, S.; Liekens, S.; Balzarini, J.; Borbone, N. Screening platform toward new anti-HIV aptamers set on molecular docking and fluorescence quenching techniques. Anal. Chem., 2016, 88(4), 2327-2334.
[http://dx.doi.org/10.1021/acs.analchem.5b04268] [PMID: 26810800]
[117]
Bai, J.; Luo, Y.; Wang, X.; Li, S.; Luo, M.; Yin, M.; Zuo, Y.; Li, G.; Yao, J.; Yang, H.; Zhang, M.; Wei, W.; Wang, M.; Wang, R.; Fan, C.; Zhao, Y. A protein-independent fluorescent RNA aptamer reporter system for plant genetic engineering. Nat. Commun., 2020, 11(1), 3847.
[http://dx.doi.org/10.1038/s41467-020-17497-7] [PMID: 32737299]
[118]
Chen, Y.; Gao, P.; Pan, W.; Shi, M.; Liu, S.; Li, N.; Tang, B. Polyvalent spherical aptamer engineered macrophages: X-ray-actuated phenotypic transformation for tumor immunotherapy. Chem. Sci., 2021, 12(41), 13817-13824.
[http://dx.doi.org/10.1039/D1SC03997K] [PMID: 34760167]
[119]
Li, H.; Wang, M.; Shi, T.; Yang, S.; Zhang, J.; Wang, H.H.; Nie, Z. A DNA-Mediated Chemically Induced Dimerization (D-CID) nanodevice for nongenetic receptor engineering to control cell behavior. Angew. Chem. Int. Ed., 2018, 57(32), 10226-10230.
[http://dx.doi.org/10.1002/anie.201806155] [PMID: 29944203]
[120]
Pfeiffer, F.; Rosenthal, M.; Siegl, J.; Ewers, J.; Mayer, G. Customised nucleic acid libraries for enhanced aptamer selection and performance. Curr. Opin. Biotechnol., 2017, 48, 111-118.
[http://dx.doi.org/10.1016/j.copbio.2017.03.026] [PMID: 28437710]
[121]
Zhang, Y.; Xie, X.; Yeganeh, P.N.; Lee, D.J.; Valle-Garcia, D.; Meza-Sosa, K.F.; Junqueira, C.; Su, J.; Luo, H.R.; Hide, W.; Lieberman, J. Immunotherapy for breast cancer using EpCAM aptamer tumor-targeted gene knockdown. Proc. Natl. Acad. Sci. USA, 2021, 118(9), e2022830118.
[http://dx.doi.org/10.1073/pnas.2022830118] [PMID: 33627408]
[122]
Sun, L.; Zhao, Q. Direct fluorescence anisotropy approach for aflatoxin B1 detection and affinity binding study by using single tetramethylrhodamine labeled aptamer. Talanta, 2018, 189, 442-450.
[http://dx.doi.org/10.1016/j.talanta.2018.07.036] [PMID: 30086944]
[123]
Rangel, A.E.; Chen, Z.; Ayele, T.M.; Heemstra, J.M. in vitro selection of an XNA aptamer capable of small-molecule recognition. Nucleic Acids Res., 2018, 46(16), 8057-8068.
[http://dx.doi.org/10.1093/nar/gky667] [PMID: 30085205]
[124]
Song, M.; Li, Y.; Gao, R.; Liu, J.; Huang, Q. De novo design of DNA aptamers that target okadaic acid (OA) by docking-then-assembling of single nucleotides. Biosens. Bioelectron., 2022, 215, 114562.
[http://dx.doi.org/10.1016/j.bios.2022.114562] [PMID: 35870338]
[125]
Tivon, Y.; Falcone, G.; Deiters, A. Protein labeling and crosslinking by covalent aptamers. Angew. Chem. Int. Ed., 2021, 60(29), 15899-15904.
[http://dx.doi.org/10.1002/anie.202101174] [PMID: 33928724]
[126]
Xiong, H.; Yan, J.; Cai, S.; He, Q.; Peng, D.; Liu, Z.; Liu, Y. Cancer protein biomarker discovery based on nucleic acid aptamers. Int. J. Biol. Macromol., 2019, 132, 190-202.
[http://dx.doi.org/10.1016/j.ijbiomac.2019.03.165] [PMID: 30926499]
[127]
Yu, Q.; Liu, M.; Wei, S.; Xiao, H.; Wu, S.; Ke, K.; Huang, X.; Qin, Q.; Li, P. Identification of major capsid protein as a potential biomarker of grouper iridovirus-infected cells using aptamers selected by SELEX. Front. Microbiol., 2019, 10, 2684.
[http://dx.doi.org/10.3389/fmicb.2019.02684] [PMID: 31849862]
[128]
Liu, M.; Wang, Z.; Li, S.; Deng, Y.; He, N. Identification of PHB2 as a potential biomarker of luminal a breast cancer cells using a cell-specific aptamer. ACS Appl. Mater. Interfaces, 2022, 14(46), 51593-51601.
[http://dx.doi.org/10.1021/acsami.2c12291] [PMID: 36346944]
[129]
Dong, H.; Han, L.; Wu, Z.S.; Zhang, T.; Xie, J.; Ma, J.; Wang, J.; Li, T.; Gao, Y.; Shao, J.; Sinko, P.J.; Jia, L. Biostable aptamer rings conjugated for targeting two biomarkers on circulating tumor cells in vivo with great precision. Chem. Mater., 2017, 29(24), 10312-10325.
[http://dx.doi.org/10.1021/acs.chemmater.7b03044]
[130]
Zheng, H.; GhavamiNejad, A.; GhavamiNejad, P.; Samarikhalaj, M.; Giacca, A.; Poudineh, M. Hydrogel Microneedle-assisted assay integrating aptamer probes and fluorescence detection for reagentless biomarker quantification. ACS Sens., 2022, 7(8), 2387-2399.
[http://dx.doi.org/10.1021/acssensors.2c01033] [PMID: 35866892]
[131]
Varty, K.; O’Brien, C.; Ignaszak, A. Breast cancer aptamers: Current sensing targets, available aptamers, and their evaluation for clinical use in diagnostics. Cancers, 2021, 13(16), 3984.
[http://dx.doi.org/10.3390/cancers13163984] [PMID: 34439139]
[132]
Shigdar, S.; Agnello, L.; Fedele, M.; Camorani, S.; Cerchia, L. Profiling cancer cells by Cell-SELEX: Use of aptamers for discovery of actionable biomarkers and therapeutic applications thereof. Pharmaceutics, 2021, 14(1), 28.
[http://dx.doi.org/10.3390/pharmaceutics14010028] [PMID: 35056924]
[133]
Pang, X.; Cui, C.; Wan, S.; Jiang, Y.; Zhang, L.; Xia, L.; Li, L.; Li, X.; Tan, W. Bioapplications of Cell-SELEX-Generated aptamers in cancer diagnostics, therapeutics, theranostics and biomarker discovery: A comprehensive review. Cancers, 2018, 10(2), 47.
[http://dx.doi.org/10.3390/cancers10020047] [PMID: 29425173]
[134]
Reinholt, S.J.; Craighead, H.G. Microfluidic device for aptamer-based cancer cell capture and genetic mutation detection. Anal. Chem., 2018, 90(4), 2601-2608.
[http://dx.doi.org/10.1021/acs.analchem.7b04120] [PMID: 29323871]
[135]
Pourmadadi, M.; Soleimani Dinani, H.; Saeidi Tabar, F.; Khassi, K.; Janfaza, S.; Tasnim, N.; Hoorfar, M. Properties and Applications of graphene and its derivatives in biosensors for cancer detection: A comprehensive review. Biosensors, 2022, 12(5), 269.
[http://dx.doi.org/10.3390/bios12050269] [PMID: 35624570]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy