Contributions of a disulfide bond to the structure, stability, and dimerization of human IgG1 antibody CH3 domain

Recombinant human monoclonal antibodies have become important protein-based therapeutics for the treatment of various diseases. The antibody structure is complex, consisting of β-sheet rich domains stabilized by multiple disulfide bridges. The dimerization of the CH3 domain in the constant region of the heavy chain plays a pivotal role in the assembly of an antibody. This domain contains a single buried, highly conserved disulfide bond. This disulfide bond was not required for dimerization, since a recombinant human CH3 domain, even in the reduced state, existed as a dimer. Spectroscopic analyses showed that the secondary and tertiary structures of reduced and oxidized CH3 dimer were similar, but differences were observed. The reduced CH3 dimer was less stable than the oxidized form to denaturation by guanidinium chloride (GdmCl), pH, or heat. Equilibrium sedimentation revealed that the reduced dimer dissociated at lower GdmCl concentration than the oxidized form. This implies that the disulfide bond shifts the monomer–dimer equilibrium. Interestingly, the dimer–monomer dissociation transition occurred at lower GdmCl concentration than the unfolding transition. Thus, disulfide bond formation in the human CH3 domain is important for stability and dimerization. Here we show the importance of the role played by the disulfide bond and how it affects the stability and monomer–dimer equilibrium of the human CH3 domain. Hence, these results may have implications for the stability of the intact antibody

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL