Title

Authors

Vinodh B Kurella & Reddy Gali*

Affiliation

Center for Biomedical Informatics and The Harvard Clinical and Translational Science Center, Harvard Medical School, Boston MA-02115, USA

Email

rgali@hms.harvard.edu; *Corresponding author

Article Type

Hypothesis

Date

Received December 13, 2013; Revised March 17, 2014; Accepted March 18, 2014; Published April 23, 2014

No universal strategy exists for humanizing mouse antibodies, and most approaches are based on primary sequence alignment and grafting. Although this strategy theoretically decreases the immunogenicity of mouse antibodies, it neither addresses conformational changes nor steric clashes that arise due to grafting of human germline frameworks to accommodate mouse CDR regions. To address these issues, we created and tested a structure-based biologic design approach using a de novo homology model to aid in the humanization of 17 unique mouse antibodies. Our approach included building a structure-based de novo homology model from the primary mouse antibody sequence, mutation of the mouse framework residues to the closest human germline sequence and energy minimization by simulated annealing on the humanized homology model. Certain residues displayed force field errors and revealed steric clashes upon closer examination. Therefore, further mutations were introduced to rationally correct these errors. In conclusion, use of de novo antibody homology modeling together with simulated annealing improved the ability to predict conformational and steric clashes that may arise due to conversion of a mouse antibody into the humanized form and would prevent its neutralization when administered in vivo. This design provides a robust path towards the development of a universal strategy for humanization of mouse antibodies using computationally derived antibody homologous structures. 

Keywords

antibodies, antibody humanization, antibody engineering, antibody design, structure-based homology model, simulated annealing, PIGS, Rosetta.

Citation

Kurella & Gali,   Bioinformation 10(4): 180-186 (2014)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.