Multiscale Brownian dynamics simulations reveal effects of macromolecular crowding on protein–ligand association
- 1Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Heidelberg 69118, Germany
- 2Faculty of Physics, Heidelberg University, Heidelberg 69120, Germany
- 3Faculties of Engineering Sciences and Biosciences, Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg 69120, Germany
Abstract
Macromolecular crowding is known to influence the diffusive, binding, kinetic, and equilibrium properties of proteins in a range of environments such as occur inside biological cells or in highly concentrated solutions of protein therapeutics. However a quantitative understanding of the effects of macromolecular crowding on the rates of protein-small molecule binding is lacking. Here, we present an adaptive model to simulate the association of two solutes in a macromolecularly crowded environment using Brownian dynamics and to compute the corresponding bimolecular association rate constant. The adaptive model represents interactions with atomistic detail for short-range crowder–noncrowder interactions, while using a coarse-grained approach for long-range crowder–noncrowder interactions and all crowder–crowder interactions. This multiscale approach is integrated in the Simulation of Diffusional Association (SDA) software for Brownian dynamics simulation of biomolecular diffusion. We applied this model to compute the rates of association for two contrasting protein-small molecule inhibitor complexes in environments varying in macromolecular crowder type and density, and we used Markov State modeling of the encounter trajectories to analyze the binding pathways. The results reveal competing effects: While the presence of the crowders can hinder ligand binding by reducing the translational diffusion rates of the solutes and obstructing the protein binding sites, crowders can also enhance the bimolecular association rate by other mechanisms, including channeling and the caging effect.
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BibTeX
@article{beccaria2026multiscale,
title = {{Multiscale Brownian dynamics simulations reveal effects of macromolecular crowding on protein–ligand association}},
author = {Riccardo Beccaria and Abraham Muñiz-Chicharro and Rebecca C. Wade},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2617950123},
url = {https://doi.org/10.1073/pnas.2617950123},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multiscale Brownian dynamics simulations reveal effects of macromolecular crowding on protein–ligand association
AU - Riccardo Beccaria
AU - Abraham Muñiz-Chicharro
AU - Rebecca C. Wade
PY - 2026
DA - 2026/10/30
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2617950123
UR - https://doi.org/10.1073/pnas.2617950123
AB - Macromolecular crowding is known to influence the diffusive, binding, kinetic, and equilibrium properties of proteins in a range of environments such as occur inside biological cells or in highly concentrated solutions of protein therapeutics. However a quantitative understanding of the effects of macromolecular crowding on the rates of protein-small molecule binding is lacking. Here, we present an adaptive model to simulate the association of two solutes in a macromolecularly crowded environment using Brownian dynamics and to compute the corresponding bimolecular association rate constant. The adaptive model represents interactions with atomistic detail for short-range crowder–noncrowder interactions, while using a coarse-grained approach for long-range crowder–noncrowder interactions and all crowder–crowder interactions. This multiscale approach is integrated in the Simulation of Diffusional Association (SDA) software for Brownian dynamics simulation of biomolecular diffusion. We applied this model to compute the rates of association for two contrasting protein-small molecule inhibitor complexes in environments varying in macromolecular crowder type and density, and we used Markov State modeling of the encounter trajectories to analyze the binding pathways. The results reveal competing effects: While the presence of the crowders can hinder ligand binding by reducing the translational diffusion rates of the solutes and obstructing the protein binding sites, crowders can also enhance the bimolecular association rate by other mechanisms, including channeling and the caging effect.
SN - 0027-8424
SN - 1091-6490
ER -