Construction of a variable fragment (Fv)-immunoglobulin A (IgA) anti-receptor binding domain (RBD) SARS-CoV-2 library based on IgA from Indonesian COVID-19 survivors.

Despite entering the post-pandemic phase, SARS-CoV-2 remains a treatment challenge due to evolving mutations and immune evasion, leading to the emergence of antibody-resistant variants. This study aims to computationally construct a human Fv against various emerged variants of SARS-CoV-2 based on IgA sequences from Indonesian COVID-19 survivors. Survivor's saliva and plasma were purified using affinity chromatography to isolate anti-SARS-CoV-2 IgA. The IgA components, heavy and light chains, were isolated using SDS-PAGE and confirmed by Western Blot. They were extracted, digested with trypsin and chymotrypsin, and sequenced using LC MS/MS. Full Fvs were constructed based on IgA sequence obtained and covered with database and reference sequences to generate an Fv Library. The selection of the Fv Library was performed based on modelling, developability, and molecular docking analysis against various RBD variants. The study identified 9 potential Fvs with strong binding affinities to RBD-SARS-CoV-2 across all variants with RMSD values of CDR and Framework of Fv model structures <0.5 Å and developability scores within the safe therapeutic range. FVIGA0289, one of the top candidates, had binding affinities (ΔG) of -17.5, -16.3, -15.6, -16.6, -17.4, and -17.6 kcal/mol for the Wuhan, alpha, beta, gamma, delta, and omicron (XBB.1.5) variants, respectively. In conclusion, the use of antibody information isolated from Indonesian patients has successfully facilitated the computational construction of IgA-based Fv candidates with strong binding to multiple SARS-CoV-2 variants, supported by promising structural models and developability.