Molecular modelling and optimization of a high-affinity nanobody targeting the nipah virus fusion protein through in silico site-directed mutagenesis

Nipah virus (NiV) is a re-emerging zoonotic pathogen with a high mortality rate and no effective treatments, prompting the search for new antiviral strategies. While conventional antiviral drugs are often limited by issues such as poor specificity, off-target effects, and resistance development, nanobodies offer distinct advantages. These small, single-domain antibodies exhibit high specificity and stability, making them ideal candidates for antiviral therapy. The NiV fusion protein (NiVF) is a crucial target for nanobodies due to its vital role in infection. Thus, we aimed to design a high affinity nanobody targeting NiVF using computational methods. Molecular docking identified the lead NB with the highest binding energy to NiVF. The complementarity determining regions (CDRs) of the lead NB underwent two rounds of in silico site-directed mutagenesis generating a highaffinity engineered NB. Subsequent re-docking, molecular dynamics (MD) simulations, and various in silico evaluations, of the selected engineered NB-NiVF complex were performed. After mutations, results showed that the lead (native) NB, initially with a binding energy of -85.2 kcal.mol- 1, was optimized to an engineered NB with a higher binding energy of -99.65 kcal.mol- 1. Additionally, the engineered NB has more favorable physicochemical properties, exhibited a more stable (in a 200-ns MD simulation) and stronger molecular interactions than the native NB, suggesting a favorable mutation and enhancement of the potential neutralization activity of the engineered NB. This study highlights the use of computational methods to design an optimized high-affinity NB and the potential of NB-based antivirals against NiV, necessitating further experimental validation.