267
Views
20
CrossRef citations to date
0
Altmetric
Articles

Prediction of the binding affinity of aptamers against the influenza virus

ORCID Icon, , &
Pages 51-62 | Received 24 Oct 2018, Published online: 14 Jan 2019

References

  • W.H. Tan, M.J. Donovan, and J.H. Jiang, Aptamers from cell-based selection for bioanalytical applications, Chem. Rev. 113 (2013), pp. 2842–2862.
  • X.L. Yu, H.Q. Yang, and X.W. Huang, Novel method for structure−activity relationship of aptamer sequences for human prostate cancer, ACS Omega 3 (2018), pp.10002−10007.
  • X.L. Yu, R.Q. Yu, L.J. Tang, Q.P. Guo, Y. Zhang, Y. Zhou, Q. Yang, X.X. He, X.H. Yang, and K.M. Wang, Recognition of candidate aptamer sequences for human hepatocellular carcinoma in SELEX screening using structure–activity relationships, Chemom. Intell. Lab. Syst. 136 (2014), pp. 10–14.
  • H. Shi, W. Cui, X. He, Q. Guo, K. Wang, S. Ye, and J. Tang, Whole cell-SELEX aptamers for highly specific fluorescence molecular imaging of carcinomas in vivo, PLoS ONE 8 (2013), pp. e70476.
  • R. Huang, Z. Chen, M. Liu, Y. Deng, S. Li, and N. He, The aptamers generated from HepG2 cells, Sci. China Chem. 60 (2017), pp. 786–792.
  • Q.P. Guo, X.D. Liu, Y.Y. Tan, K.M. Wang, X.H. Yang, Y. Zhou, L. Ye, and X.Y. Zhao, Selection of aptamers for human hepatocellular carcinoma with high specificity, Chin. Sci. Bull. 58 (2013), pp. 2745–2750.
  • B. Musafia, R. Oren-Banaroya, and S. Noiman, Designing anti-influenza aptamers: Novel quantitative structure activity relationship approach gives insights into aptamer–virus interaction, PLoS ONE 9 (2014), pp. e97696.
  • J.M. Binning, D.W. Leung, and G.K. Amarasinghe, Aptamers in virology: Recent advances and challenges, Front. Microbiol. 3 (2012), pp. 29.
  • P.R. Bouchard, R.M. Hutabarat, and K.M. Thompson, Discovery and development of therapeutic aptamers, Annu. Rev. Pharmacol. Toxicol. 50 (2010), pp. 237–257.
  • P.J. Bates, D.A. Laber, D.M. Miller, S.D. Thomas, and J.O. Trent, Discovery and development of the G-rich oligonucleotide AS1411 as a novel treatment for cancer, Exp. Mol. Pathol. 86 (2009), pp. 151–164.
  • A.D. Ellington and J.W. Szostak, In vitro selection of RNA molecules that bind specific ligands, Nature 346 (1990), pp. 818–822.
  • C. Tuerk and L. Gold, Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase, Science 249 (1990), pp. 505–510.
  • X. Fang and W. Tan, Aptamers generated from cell-SELEX for molecular medicine: A chemical biology approach, Acc. Chem. Res. 43 (2010), pp. 48–57.
  • K. Ninomiya, K. Kaneda, S. Kawashima, Y. Miyachi, C. Ogino, and N. Shimizu, Cell-SELEX based selection and characterization of DNA aptamer recognizing human hepatocarcinoma, Bioorg. Med. Chem. Lett. 23 (2013), pp. 1797–802.
  • C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul, A practical overview of quantitative structure-activity relationship, EXCLI J. 8 (2009), pp. 74–88.
  • B.Q. Li, Y.C. Zhang, G.H. Huang, W.R. Cui, N. Zhang, and Y.D. Cai, Prediction of aptamer-target interacting pairs with pseudo-amino acid composition, PLoS ONE 9 (2014), pp. e86729.
  • M. Daszykowski, S Serneels, K Kaczmarek, PV Espen, C Croux, and B. Walczak, TOMCAT: A MATLAB toolbox for multivariate calibration techniques, Chemom. Intell. Lab. Syst. 85 (2007), pp. 269–277.
  • R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, DRAGON for Windows (Software for the Calculation of Molecular Descriptors), Version 6.0. TALETE srl, Milan, Italy, 2006.
  • S. Mofavvaz, M.R. Sohrabi, and A. Nezamzadeh-Ejhieh, New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method, Spectrochim. Acta A Mol. Biomol. Spectrosc. 182 (2017), pp. 105–115.
  • D.A. Sarigiannis, K. Papadaki, P. Kontoroupis, and S.P. Karakitsios, Development of QSARs for parameterizing physiology based toxicokinetic models, Food Chem. Toxicol. 106 (2017), pp. 114–124.
  • D.S. Jat, P. Dhaka, and A. Limbo, Applications of statistical techniques and artificial neural networks: A review, J. Stat. Manag. Syst. 21 (2018), pp. 639–645.
  • M. Mizera, A. Krause, P. Zalewski, R. Skibiński, and J. Cielecka-Piontek, Quantitative structure-retention relationship model for the determination of naratriptan hydrochloride and its impurities based on artificial neural networks coupled with genetic algorithm, Talanta 164 (2017), pp. 164–174.
  • D.A. Winkler, F.R. Burden, B. Yan, R. Weissleder, C. Tassa, S. Shaw, and V.C. Epa, Modelling and predicting the biological effects of nanomaterials, SAR QSAR Environ. Res. 12 (2014), pp. 161–172.
  • Y.Y. Xu, X.L. Yu, and S.H. Zhang, QSAR models of reaction rate constants of alkenes with ozone and hydroxyl radical, J. Braz. Chem. Soc. 24 (2013), pp. 1781–1788.
  • M. Hamadache, S. Hanini, O. Benkortbi, A. Amrane, L. Khaouane, and C.S. Moussa, Artificial neural network-based equation to predict the toxicity of herbicides on rats, Chemom. Intell. Lab. Syst. 154 (2016), pp. 7–15.
  • X.L. Yu and L. Huang, Prediction of the onset temperature of decomposition of lubricant additives, J. Therm. Anal. Calorim. 130 (2017), pp. 943–947.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.