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Turkish Journal of Biology

Abstract

Background/aim: In recent years, single-domain antibodies, also known as nanobodies, have emerged as an alternative to full immunoglobulin Gs (IgGs), due to their various advantages including increased solubility, faster clearance, and cheaper production. Nanobodies are generally derived from the variable domain of the camelid heavy-chain-only immunoglobulin Gs (hcIgGs). Due to the high sequence homology between variable heavy chains of camelids (VHHs) and humans (VHs), hcIgGs are ideal candidates for nanobody development. However, further examination is needed to understand the structural differences between VHs and VHHs. This analysis is essential for nanobody engineering to mitigate potential immunogenicity while preserving stability, functionality, and antigen specificity.Materials and methods: We obtained VH and VHH sequences of various camelid and non-camelid mammalian antibodies from public databases and used multiple sequence alignment based on the Chothia numbering scheme. Aligned sequences were subjected to diverse analyses encompassing paratope length, binding prediction, motif, disulfide bridge, salt bridge profiling, and physicochemical characteristic distribution. Logistic regression coupled with the Boruta–random forest algorithm facilitated the comprehensive examination of physicochemical properties.Results: Our findings revealed longer, less variable paratope sequences in VHHs, along with specific antigen binding residues with increased binding potential compared to VHs. Although the VHs showed more heterogeneous noncanonical disulfide bond patterns, the VHHs had a higher number of noncanonical disulfide bridges. Intriguingly, a typical salt bridge between the 94th and 101st positions in the VHs had a very low encounter rate in the VHHs. Surprisingly, we also identified notable differences in the physicochemical patterns of mostly conserved frameworks (FWs), especially the FW2 and FW3 regions, between VHs and VHHs.Conclusion: Our findings point to possible key sites in VHHs as candidate residues for nanobody engineering efforts.

DOI

10.55730/1300-0152.2674

Keywords

Antibody, camelid, logistic regression, machine learning, nanobody, physicochemical properties

First Page

423

Last Page

436

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