Development of a Graphene-Based Biosensor for Detecting Recombinant Cyanovirin-N
| Autor: | Elíbio L. Rech, Pedro Rodrigues de Almeida, E. S. Alves, André M. Murad, Luciano P. Silva |
|---|---|
| Přispěvatelé: | PEDRO RODRIGUES DE ALMEIDA, UFMG, ANDRE MELRO MURAD, Cenargen, LUCIANO PAULINO DA SILVA, Cenargen, ELIBIO LEOPOLDO RECH FILHO, Cenargen, ELMO SALOMÃO ALVES, UFMG. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Biotechnology
Clinical Biochemistry Antiviral protein electrochemical sensor Biosensing Techniques law.invention Genetically modified soybean Bacterial Proteins law Microbicide lcsh:TP248.13-248.65 Humans Recombinant cyanovirin-N Graphene biosensor Detection limit biology Chemistry Graphene Communication Reproducibility of Results General Medicine Electrochemical Techniques Combinatorial chemistry genetically modified soybean Electrochemical gas sensor Cyanovirin-N Electrochemical sensor recombinant cyanovirin-N Seeds biology.protein Recombinant DNA graphene biosensor Graphite Soybeans Biosensor |
| Zdroj: | Biosensors Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA Biosensors, Vol 10, Iss 206, p 206 (2020) |
| ISSN: | 2079-6374 |
| Popis: | We present a graphene-based biosensor selective to recombinant cyanovirin-N (rCV-N), an antiviral protein that has proven to be an effective microbicide to inhibit HIV replication. We modified the graphene monolayer devices with 1-pyrenebutanoic acid succinimidyl ester, which interacts with both graphene and the primary and secondary amines of antibodies. By monitoring the change in the electrical resistance of the device, we were able to detect rCV-N in solutions in the range of 0.01 to 10 ng/mL, and found that the detection limit was 0.45 pg/mL, which is much smaller than that obtained with currently available techniques. This is important for applications of this microbicide against HIV, since it may be produced at a large scale from soya bean seeds processed using the available industrial processing technologies. The sensor showed high sensitivity, selectivity, and reproducibility. |
| Databáze: | OpenAIRE |
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