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Humanization of Antibodies using a Statistical Inference Approach.

Alejandro Clavero-Álvarez1, Tomas Di Mambro2, Sergio Perez-Gaviro1,3,4

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A new "humanness score" method improves antibody humanization by analyzing sequence correlations. This approach generates humanized antibody sequences that are recognized as human, overcoming limitations of traditional CDR grafting techniques.

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Area of Science:

  • Biotechnology
  • Immunology
  • Computational Biology

Background:

  • Antibody humanization is crucial for developing therapeutic antibodies from non-human models.
  • Traditional Complementarity-Determining Regions (CDR) grafting can require back-mutations for functionality and stability.

Purpose of the Study:

  • To introduce a novel method for characterizing human antibody variable region sequences.
  • To develop a "humanness score" to differentiate human from murine antibody sequences.
  • To optimize antibody humanization by searching sequence space while preserving CDRs.

Main Methods:

  • Characterizing statistical distribution of human antibody variable region sequences using phenotypical residue correlations.
  • Defining a "humanness score" based on sequence classification performance.
  • Comparing the score against existing methods and experimental immunogenicity data.
  • Employing the humanness score as an optimization function for sequence generation.

Main Results:

  • The proposed humanness score effectively distinguishes human from murine antibody sequences.
  • The optimization protocol successfully generated humanized sequences recognized as human by homology modeling tools.
  • The humanness score outperformed alternative methods in sequence classification tasks.

Conclusions:

  • The novel humanness score offers a superior approach for antibody humanization.
  • This method facilitates the generation of more human-like antibody sequences, potentially reducing immunogenicity.
  • The optimization strategy aids in developing safer and more effective therapeutic antibodies.