An engineered M13 phage–rGO electrochemical biosensor for rapid detection of viral protein in complex matrices

The demand for rapid and scalable biosensing technologies has motivated the development of antibody-free platforms capable of operating in complex sample environments. Here, we report an electrochemical biosensor based on engineered M13 bacteriophages displaying a SARS-CoV-2 spike S1-binding peptide immobilized on a reduced graphene oxide (rGO) transducer. The sensor employs a chemiresistive detection mechanism under a fixed low-voltage bias, enabling rapid electrical readout following target binding. Detection of S1 protein was achieved in buffer and in spiked complex matrices, including fetal bovine serum, pasteurized milk, and wastewater, demonstrating matrix tolerance under the tested conditions. The biosensor response is evaluated using a statistically defined binary detection criterion, with an operational limit of detection of 10⁻<sup>4</sup> pg/mL in buffer. Compared to a previously reported antibody-functionalized rGO sensor fabricated using the same platform, the phage-based biosensor exhibits comparable sensitivity while offering advantages in genetic tunability and production scalability. While the present study focuses on proof-of-concept validation using spiked samples, these results highlight the potential of engineered phage-graphene interfaces as adaptable biorecognition elements for rapid electrochemical protein sensing in complex environments.

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Publication Details

Journal
Scientific Reports
Published
2026-02-16
DOI
https://doi.org/10.1038/s41598-026-37008-w
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

An engineered M13 phage–rGO electrochemical biosensor for rapid detection of viral protein in complex matrices

Selvakumar Palanisamy, Lina Tizani, Ahmed F. Yousef, Habiba Alsafar et al.
Scientific Reports
Advanced biosensing and bioanalysis techniques
article

An engineered M13 phage–rGO electrochemical biosensor for rapid detection of viral protein in complex matrices

Selvakumar Palanisamy, Lina Tizani, Ahmed F. Yousef, Habiba Alsafar, Mohamed Helal, Hamda Alshehhi, Shadi W. Hasan
article en

Abstract

The demand for rapid and scalable biosensing technologies has motivated the development of antibody-free platforms capable of operating in complex sample environments. Here, we report an electrochemical biosensor based on engineered M13 bacteriophages displaying a SARS-CoV-2 spike S1-binding peptide immobilized on a reduced graphene oxide (rGO) transducer. The sensor employs a chemiresistive detection mechanism under a fixed low-voltage bias, enabling rapid electrical readout following target binding. Detection of S1 protein was achieved in buffer and in spiked complex matrices, including fetal bovine serum, pasteurized milk, and wastewater, demonstrating matrix tolerance under the tested conditions. The biosensor response is evaluated using a statistically defined binary detection criterion, with an operational limit of detection of 10⁻<sup>4</sup> pg/mL in buffer. Compared to a previously reported antibody-functionalized rGO sensor fabricated using the same platform, the phage-based biosensor exhibits comparable sensitivity while offering advantages in genetic tunability and production scalability. While the present study focuses on proof-of-concept validation using spiked samples, these results highlight the potential of engineered phage-graphene interfaces as adaptable biorecognition elements for rapid electrochemical protein sensing in complex environments.

Scientific ReportsVol. 16(1)
Khalifa University of Science and Technology (AE)
Khalifa University of Science, Technology and Research, Center for Membranes and Advanced Water Technology, Khalifa University
Clean water and sanitation
Openalex Percentile: Top 54%
Advanced biosensing and bioanalysis techniques
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An engineered M13 phage–rGO electrochemical biosensor for rapid detection of viral protein in complex matrices — Selvakumar Palanisamy, Lina Tizani, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS