ModiBodies: A computational method for modifying nanobodies to improve their antigen binding affinity and specificity

Nanobodies are special derivatives of antibodies, which consist of only a single chain. Their hydrophilic side prevents them from having the solubility and aggregation problems of conventional antibodies, and they retain the similar size and affinity of the binding area to the antigen. Nanobodies have become of considerable interest for next-generation biotechnological tools for antigen recognition. They can be easily engineered due to their high stability and compact size. They have three complementarity determining regions, CDRs, which are enlarged to provide a similar binding surface to that of regular antibodies. The binding residues are more exposed to the environment. One common strategy to improve protein solubility is to replace hydrophobic residues with hydrophilic ones on the binding surface which contributes to both stability and solubility of nanobodies.[1] Here, we propose an algorithm that uses the 3D structures of protein-nanobody complexes as the initial structures and by successive mutations in the CDR domains to find optimum binding amino acids for hypervariable residues of CDRs to increase the binding affinity and nanobody selectivity. We used the MDM4-VH9 complex, (PDB id 2VYR), fructose-bisphosphate aldolase from Trypanosoma congolense, (PDB id 5O0W), and human lysozyme, (PDB id 4I0C). as benchmark studies and identified similar amino acid patterns in hypervariable residues of CDRs with experimentally optimized ones. According to this method, better binding nanobodies can be generated by using this algorithm in a short time. We suggest that this method can complement existing immune and synthetic library-based methods, without the need of experiments or large libraries.