# Proof-of-Concept Nanoparticle-Based Biosensor for Detecting the African Swine Fever Virus Across Multiple Genotypes Using In Silico and In Vitro Approaches

## TL;DR

This paper presents a new biosensor using gold nanoparticles to detect the African Swine Fever Virus across different genotypes, combining computer modeling and lab experiments.

## Contribution

A novel biosensor design for ASFV detection using in silico and in vitro methods to optimize probe performance across multiple genotypes.

## Key findings

- Probes 2 and 5 showed the strongest performance with detection of 550 copies and no cross-reactivity.
- GC content was significantly associated with sensitivity (ρ = −0.80, p = 0.016).
- The biosensor demonstrated strong binding across multiple ASFV genotypes.

## Abstract

African swine fever
virus (ASFV) is a viral hemorrhagic disease
with high lethality in domestic and wild swine, posing a critical
threat to global food security and livestock economies. Rapid and
accurate detection of ASFV is crucial for effective containment of
outbreaks. This study evaluated a gold nanoparticle-based biosensor
for the detection of ASFV by targeting the p72 gene using eight oligonucleotide
probes. The objective was to identify optimal probes with high sensitivity
and specificity, and broad genotypic coverage. Clustal Omega was used
to perform multiple sequence alignments between each probe and diverse
ASFV genomes. Percentage identity matrices were generated and visualized
through heatmaps to assess hybridization strength across genotypes.
The biosensor was then tested with synthetic ASFV DNA at a 5 min reaction
time, using spectrophotometric analysis to evaluate detection. Sensitivity
was measured through serial dilutions of target DNA, and specificity
was confirmed using nontarget bacterial DNA. Probes 2 (40 bp, 50.0%
GC content) and 5 (60 bp, 54.2% GC content) demonstrated the strongest
overall performance, achieving detection of 550 copies with no cross-reactivity
and strong binding across multiple ASFV genotypes. Statistical analysis
using Spearman’s rank correlation demonstrated that GC content
was significantly associated with sensitivity (ρ = −0.80, p = 0.016), while probe length, secondary structure stability,
and binding advantage showed no significant relationships. This study
underscored the importance of integrating genomic alignment tools
with experimental biosensor validation to enhance probe design.