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The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen
Herein, we report a method of combining bioinformatics and biosensing technologies to select aptamers against prostate specific antigen (PSA). The main objective of this study is to select DNA aptamers with higher binding affinity for PSA by using the proposed method. Based on the five known sequenc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5387809/ https://www.ncbi.nlm.nih.gov/pubmed/28459059 http://dx.doi.org/10.1155/2017/5041683 |
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author | Hsieh, Pi-Chou Lin, Hui-Ting Chen, Wen-Yih Tsai, Jeffrey J. P. Hu, Wen-Pin |
author_facet | Hsieh, Pi-Chou Lin, Hui-Ting Chen, Wen-Yih Tsai, Jeffrey J. P. Hu, Wen-Pin |
author_sort | Hsieh, Pi-Chou |
collection | PubMed |
description | Herein, we report a method of combining bioinformatics and biosensing technologies to select aptamers against prostate specific antigen (PSA). The main objective of this study is to select DNA aptamers with higher binding affinity for PSA by using the proposed method. Based on the five known sequences of PSA-binding aptamers, we adopted the functions of reproduction and crossover in the genetic algorithm to produce next-generation sequences for the computational and experimental analysis. RNAfold web server was utilized to analyze the secondary structures, and the 3-dimensional molecular models of aptamer sequences were generated by using RNAComposer web server. ZRANK scoring function was used to rerank the docking predictions from ZDOCK. The biosensors, the quartz crystal microbalance (QCM) and a surface plasmon resonance (SPR) instrument, were used to verify the binding ability of selected aptamer for PSA. By carrying out the simulations and experiments after two generations, we obtain one aptamer that can have the highest binding affinity with PSA, which generates almost 2-fold and 3-fold greater measured signals than the responses produced by the best known DNA sequence in the QCM and SPR experiments, respectively. |
format | Online Article Text |
id | pubmed-5387809 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-53878092017-04-30 The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen Hsieh, Pi-Chou Lin, Hui-Ting Chen, Wen-Yih Tsai, Jeffrey J. P. Hu, Wen-Pin Biomed Res Int Research Article Herein, we report a method of combining bioinformatics and biosensing technologies to select aptamers against prostate specific antigen (PSA). The main objective of this study is to select DNA aptamers with higher binding affinity for PSA by using the proposed method. Based on the five known sequences of PSA-binding aptamers, we adopted the functions of reproduction and crossover in the genetic algorithm to produce next-generation sequences for the computational and experimental analysis. RNAfold web server was utilized to analyze the secondary structures, and the 3-dimensional molecular models of aptamer sequences were generated by using RNAComposer web server. ZRANK scoring function was used to rerank the docking predictions from ZDOCK. The biosensors, the quartz crystal microbalance (QCM) and a surface plasmon resonance (SPR) instrument, were used to verify the binding ability of selected aptamer for PSA. By carrying out the simulations and experiments after two generations, we obtain one aptamer that can have the highest binding affinity with PSA, which generates almost 2-fold and 3-fold greater measured signals than the responses produced by the best known DNA sequence in the QCM and SPR experiments, respectively. Hindawi 2017 2017-03-28 /pmc/articles/PMC5387809/ /pubmed/28459059 http://dx.doi.org/10.1155/2017/5041683 Text en Copyright © 2017 Pi-Chou Hsieh et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hsieh, Pi-Chou Lin, Hui-Ting Chen, Wen-Yih Tsai, Jeffrey J. P. Hu, Wen-Pin The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title | The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title_full | The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title_fullStr | The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title_full_unstemmed | The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title_short | The Combination of Computational and Biosensing Technologies for Selecting Aptamer against Prostate Specific Antigen |
title_sort | combination of computational and biosensing technologies for selecting aptamer against prostate specific antigen |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5387809/ https://www.ncbi.nlm.nih.gov/pubmed/28459059 http://dx.doi.org/10.1155/2017/5041683 |
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