iMano

Monoclonal antibodies (mAbs) and nanobodies (Nbs) are core therapeutic tools, but animal-derived antibodies trigger immunogenicity (e.g., HAMA responses) in humans. Antibody humanization is critical to mitigate this risk.

Conventional CDR grafting is labor-intensive (requiring iterative back mutation screening) and relies on resolved antigen-antibody complex structures. iMano addresses these limitations via computational biology, enabling efficient humanization without complex experimental structures.

Antibody humanization is a critical step in therapeutic antibody development, aiming to minimize immunogenicity arising from non-human sequences. However, conventional CDR grafting techniques are labor-intensive and retain extensive framework beyond CDR regions. Here, we present a computational strategy, iMano, for antibody humanization that streamlines the traditionally iterative trial-and-error process. By leveraging RMSD-based structural evaluation and integrating residue-level positional and energetic information into residue selection, iMano enables rational framework design without requiring experimentally resolved antigen-antibody complex structures. In four representative cases, including a nanobody, the approach produced highly humanized variants that maintained strong binding activity, with only a limited number of candidates requiring experimental screening. These results demonstrate the potential of this computational strategy to accelerate antibody humanization while preserving specificity and affinity, offering a flexible and broadly useful solution for antibody engineering across diverse formats and antigen targets.