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A multi-scale numerical approach to study monoclonal antibodies in solution
Marco Polimeni1, Emanuela Zaccarelli2, Alessandro Gulotta1
1Division of Physical Chemistry, Lund University, Lund, Sweden.
APL Bioengineering
|March 1, 2024
Summary
This study introduces a fast computational model for protein solutions, crucial for understanding high-concentration behavior and protein interactions. The model efficiently simulates many proteins, aiding in predicting phase behavior and self-assembly.
Area of Science:
- Computational chemistry
- Biophysics
- Protein science
Background:
- Accurate computational models are vital for understanding protein solution behavior, especially at high concentrations.
- Current detailed models face computational limitations in simulating complex biological systems.
Purpose of the Study:
- To develop an efficient coarse-grained computational strategy for investigating monoclonal antibody solutions.
- To enable large-scale simulations for exploring protein-protein interactions and phase behavior.
Main Methods:
- A multi-scale numerical approach connecting all-atom and amino-acid levels to coarse-grained bead models.
- Simulations of many-protein systems to explore high-concentration regimes and ionic strength effects.
Main Results:
- The developed bead models accurately reproduce key protein properties while significantly reducing computational cost.
- The strategy allows for extensive simulations, facilitating comparisons with experimental data at high protein concentrations.
Conclusions:
- The efficient coarse-grained models provide valuable insights into protein solution behavior, protein-protein interactions, and self-assembly.
- This approach is particularly effective for studying monoclonal antibody solutions under varying ionic strengths and charge conditions.
