Computational engineering the binding affinity of Adalimumab monoclonal antibody for designing potential biosimilar candidate.

Amongst the anti-TNF-α therapy for rheumatoid arthritis and other autoimmune diseases, Adalimumab mAb is one of the best candidates. However, several risk factors are found to be associated with higher doses. Improvement of the binding properties will therefore significantly increase its therapeutic efficacy, reduce the dosage requirements, and ultimately the associated toxicity and treatment cost. Here, we proposed a systematic in silico approach of finding newer mAb variants with improved binding properties. Using various bioinformatics tools, we have identified the significant amino acid residues on Adalimumab mAb. Next, we searched for the suitability of the other residues for mutating the significant residues and from the combinations of suitable mutations, variants were designed. To find the most significant ones, binding properties of the variants were compared with the wild type Adalimumab mAb using molecular docking scrutiny and molecular dynamics simulation. Finally, structural properties between the variant and wild type were analyzed. We have identified the six most significant residues on Adalimumab mAb involved in the antigen-antibody interactions. Using the suitable mutations replacing each of these residues, we have modeled 143 variants. From several docking analyses, we have found five significant variants and after molecular dynamics simulation, one most significant variant with improved binding affinity was identified whose structural properties are similar to the wild type Adalimumab mAb. Designed variant from this study, may provide newer insights on the structure-based affinity improvements of monoclonal antibodies and likewise modifications of the Fc region will also improve the therapeutic effector functions of antibodies too.