In the past few years, improvement in computational approaches provided faster and less expensive outcomes on the identification, development, and optimization of monoclonal antibodies (mAbs). In silico methods, such as homology modeling, to predict antibody structures, identification of epitope-paratope interactions, and molecular docking are useful to generate 3D structures of the antibody–antigen complexes. It helps identify the key residues involved in the antigen–antibody complex and enable modifications to enhance the antibody binding affinity. Recent advances in computational tools for redesigning antibodies are significant resources to improve antibody biophysical properties, such as binding affinity, solubility, stability, decreasing the timeframe and costs during antibody engineering. The immunobiological market grows continuously with new molecules, both natural and new molecular formats, such as bispecific antibodies, Fc-antibody fusion proteins, and mAb fragments, requiring novel methods for designing, screening, and analyzing. Algorithms and software set the in silico techniques on the innovation frontier.
In silico antibody engineering for SARS-CoV-2 detection