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Multidomain Protein-Urea Interactions: Differences in Binding Behavior Lead to Different Destabilization Tendencies
Jiyoung Yang1,2, Oliver Burkert2, Boris Mizaikoff1
1Institute for Analytical and Bioanalytical Chemistry, University of Ulm, Ulm D-89069, Germany.
Urea destabilizes monoclonal antibodies (mAbs) by binding strongly, increasing aggregation at high temperatures. Molecular dynamics simulations reveal domain-specific interactions, aiding drug formulation development.
Area of Science:
- Biochemistry
- Computational Biology
- Pharmaceutical Sciences
Background:
- Monoclonal antibodies (mAbs) are crucial therapeutics, but their stability is affected by formulation excipients like urea.
- Understanding protein-cosolute interactions is vital for developing stable antibody formulations.
- Complex multi-domain proteins like mAbs present challenges for experimental stability analysis.
Purpose of the Study:
- To investigate the influence of urea on monoclonal antibody (mAb) stability.
- To elucidate the domain-specific interactions between urea and mAbs.
- To explore the utility of molecular dynamics (MD) simulations and Kirkwood-Buff theory in studying protein-cosolute interactions.
Main Methods:
- Combined use of molecular dynamics (MD) simulations and differential scanning fluorimetry (DSF).
- Analysis of protein-urea binding at the domain level.
- Application of Kirkwood-Buff theory to interpret simulation data.
Main Results:
- Urea, a denaturing cosolute, binds strongly to mAbs, promoting denaturation and aggregation at elevated temperatures.
- Protein-urea interactions are domain-specific, influenced by individual mAb domain surface properties.
- Lennard-Jones interactions were identified as the primary drivers of significant urea binding to mAbs.
Conclusions:
- MD simulations coupled with Kirkwood-Buff theory can effectively dissect domain-specific protein-cosolute interactions.
- This approach aids in understanding and predicting the behavior of multi-domain proteins in the presence of formulation excipients.
- The findings support the use of computational methods in antibody drug discovery and formulation development.
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