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Updated: Jul 6, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
A computational procedure for predicting excipient effects on protein-protein affinities
Gregory L Dignon1,2, Ken A Dill1,3,4
1Laufer Center for Physical and Quantitative Biology, Stony Brook University.
This study presents a computational model to predict how excipients reduce protein solution viscosity. The model successfully ranks common excipients, aiding in the development of stable biologic drugs.
Area of Science:
- Biochemistry and Pharmaceutical Sciences
- Computational Chemistry and Molecular Modeling
Background:
- Protein-protein interactions are central to biological processes and pose challenges in formulating biologic drugs like antibodies.
- Excipients, or small molecule additives, are crucial for mitigating undesirable protein association and improving drug formulation stability.
Approach:
- Developed a computationally efficient model integrating molecular dynamics (MD) simulations, binding polynomials, and thermodynamic perturbation theory.
- Utilized atomic-resolution MD simulations to capture molecular interactions and predict thermodynamic properties.
Key Points:
- The model successfully predicts and rank orders the viscosity-reducing effects of four common excipients for a specific monoclonal antibody solution.
- Demonstrated the model's utility in assessing excipient impact on protein association and phase separation.
Conclusions:
- The developed computational approach offers a valuable tool for predicting excipient efficacy in protein formulations.
- This method can guide the selection of optimal excipients and buffers to enhance the stability and manufacturability of protein-based therapeutics.
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