ABSTRACT Rift Valley fever virus (RVFV), a WHO Blueprint Priority Pathogen, continues to cause major health and economic losses in endemic regions, yet current molecular and serological diagnostics remain inaccessible or insufficiently sensitive in many settings. Surface‐enhanced Raman spectroscopy (SERS) offers high sensitivity and field‐adaptable potential. This study aimed to develop a SERS‐based RVFV detection assay by combining theoretical modelling with experimental validation. A hybrid approach integrating Schrödinger‐bioluminate and density functional theory (DFT) simulations was used to model molecular interactions within the SERS immunoprobe. Experimentally, a sandwich‐type SERS immunoassay was constructed by immobilising recombinant RVFV nucleocapsid protein (rnp) on a SERS‐active substrate to capture RVFV antibodies. A complementary AuNP/spa/4‐MBA immunoprobe was synthesised to bind captured antibodies and generate a measurable Raman signal. The immunoprobe exhibited a detection limit of 1 μg/mL for RVFV antibodies, demonstrating higher sensitivity than previously developed half‐strip lateral flow assay. Theoretical predictions of key 4‐MBA Raman vibrational modes showed good consistency with experimental spectra, supporting the validity of the simulation‐guided design. The assay successfully established the conceptual feasibility of an RVFV‐specific SERS diagnostic platform. This work presents the first SERS‐based detection strategy tailored for RVFV and the first to integrate computational modelling with assay development for this pathogen. Although preliminary, the hybrid theoretical–experimental approach provides a foundation for further optimisation, incorporation of AI‐based spectral analysis and future deployment using miniaturised field‐portable Raman devices.
Domfe et al. (2026) studied this question.