Multiscale Brownian dynamics simulations reveal effects of macromolecular crowding on protein-ligand association.

Authors

Riccardo Beccaria, Abraham Muñiz-Chicharro, Rebecca C Wade

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 44. Pages e2617950123. Nov 03, 2026. Epub Oct 30, 2026.

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.

PMID:
42961935
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.