VH-VL orientation prediction for antibody humanization candidate selection: A case study. Issue 2 (17th February 2016)

• Record Type: Journal Article
• Authors: Bujotzek, Alexander; Lipsmeier, Florian; Harris, Seth F; Benz, Jörg; Kuglstatter, Andreas; Georges, Guy
• Is Part Of: MAbs. Volume 8:Issue 2(2016)
• Page Start: 288
• Page End: 305
• Publication Date: 2016-02-17
• Subjects: antibody humanization -- antibody variable domain orientation -- CDR grafting -- monoclonal antibody; Monoclonal antibodies -- Therapeutic use -- Periodicals; Monoclonal antibodies -- Periodicals; Antibodies, Monoclonal -- Periodicals; 616.0798
• DOI: 10.1080/19420862.2015.1117720
• Languages: English
• ISSNs: 1942-0862

Abstract

Abstract: Antibody humanization describes the procedure of grafting a non-human antibody's complementarity-determining regions, i.e., the variable loop regions that mediate specific interactions with the antigen, onto a β-sheet framework that is representative of the human variable region germline repertoire, thus reducing the number of potentially antigenic epitopes that might trigger an anti-antibody response. The selection criterion for the so-called acceptor frameworks (one for the heavy and one for the light chain variable region) is traditionally based on sequence similarity. Here, we propose a novel approach that selects acceptor frameworks such that the relative orientation of the 2 variable domains in 3D space, and thereby the geometry of the antigen-binding site, is conserved throughout the process of humanization. The methodology relies on a machine learning-based predictor of antibody variable domain orientation that has recently been shown to improve the quality of antibody homology models. Using data from 3 humanization campaigns, we demonstrate that preselecting humanization variants based on the predicted difference in variable domain orientation with regard to the original antibody leads to subsets of variants with a significant improvement in binding affinity.