Literature DB >> 33671864

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction.

Carl Mieczkowski1, Alan Cheng2, Thierry Fischmann3, Mark Hsieh1, Jeanne Baker1, Makiko Uchida1, Gopalan Raghunathan1, Corey Strickland3, Laurence Fayadat-Dilman1.   

Abstract

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

Entities:  

Keywords:  antibody; computational modeling; developability; dynamic light scattering; in silico prediction; protein; self-association; self-interaction; viscosity

Year:  2021        PMID: 33671864     DOI: 10.3390/antib10010008

Source DB:  PubMed          Journal:  Antibodies (Basel)        ISSN: 2073-4468


  2 in total

1.  High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy.

Authors:  Samantha Phan; Auralee Walmer; Eudean W Shaw; Qing Chai
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 6.440

Review 2.  Improving antibody drug development using bionanotechnology.

Authors:  Emily K Makowski; John S Schardt; Peter M Tessier
Journal:  Curr Opin Biotechnol       Date:  2021-12-07       Impact factor: 10.279

  2 in total

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