Decoding the Physical Mechanisms of Protein-Small Molecule Association by Multiscale Kinetic Monte-Carlo Simulations
- 1Albert Einstein College of Medicine
Abstract
Protein-small molecule association involves multiple recognition stages governed by distinct physical interactions, yet resolving their kinetic contributions remains computationally challenging. Here, we develop a multiscale Monte Carlo framework that combines an atomistic, conformationally flexible ligand with a coarse-grained protein binding pocket and an atomistic steric envelope. Association is decomposed into three stages: molecular encounter, pocket entry, and productive binding. We evaluate the framework using ten protein-ligand complexes representing nine distinct targets, with experimentally derived association rates spanning nearly four orders of magnitude. Simulated pocket-entry rates exhibit the strongest correspondence with experimental kinetics (Pearson r = 0.617; Spearman {rho} = 0.758), whereas productive-binding rates show closer agreement in absolute magnitude. Systematic perturbations of electrostatic interactions, hydrophobic attraction, hydrogen bonding, and ligand torsional flexibility reveal stage-dependent sensitivities. Initial encounter is comparatively insensitive to these perturbations, while hydrophobic attraction and conformational flexibility influence pocket entry. Productive binding exhibits additional sensitivity to local interactions, with excessive hydrogen-bond strengthening potentially hindering native-pose formation. Probability-derived kinetic-stage barriers and normalized mechanism descriptors further reveal heterogeneous recognition behavior across protein targets. Together, this framework provides a computationally efficient and interpretable approach for investigating protein-ligand association pathways and their physical determinants across diverse molecular systems.
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BibTeX
@misc{su2026decoding,
title = {{Decoding the Physical Mechanisms of Protein-Small Molecule Association by Multiscale Kinetic Monte-Carlo Simulations}},
author = {Z. Su and Y. Wu},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.23.761358},
url = {https://www.biorxiv.org/content/10.64898/2026.10.23.761358v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Decoding the Physical Mechanisms of Protein-Small Molecule Association by Multiscale Kinetic Monte-Carlo Simulations
AU - Z. Su
AU - Y. Wu
PY - 2026
DA - 2026/10/23
PB - bioRxiv
DO - 10.64898/2026.10.23.761358
UR - https://www.biorxiv.org/content/10.64898/2026.10.23.761358v1
AB - Protein-small molecule association involves multiple recognition stages governed by distinct physical interactions, yet resolving their kinetic contributions remains computationally challenging. Here, we develop a multiscale Monte Carlo framework that combines an atomistic, conformationally flexible ligand with a coarse-grained protein binding pocket and an atomistic steric envelope. Association is decomposed into three stages: molecular encounter, pocket entry, and productive binding. We evaluate the framework using ten protein-ligand complexes representing nine distinct targets, with experimentally derived association rates spanning nearly four orders of magnitude. Simulated pocket-entry rates exhibit the strongest correspondence with experimental kinetics (Pearson r = 0.617; Spearman {rho} = 0.758), whereas productive-binding rates show closer agreement in absolute magnitude. Systematic perturbations of electrostatic interactions, hydrophobic attraction, hydrogen bonding, and ligand torsional flexibility reveal stage-dependent sensitivities. Initial encounter is comparatively insensitive to these perturbations, while hydrophobic attraction and conformational flexibility influence pocket entry. Productive binding exhibits additional sensitivity to local interactions, with excessive hydrogen-bond strengthening potentially hindering native-pose formation. Probability-derived kinetic-stage barriers and normalized mechanism descriptors further reveal heterogeneous recognition behavior across protein targets. Together, this framework provides a computationally efficient and interpretable approach for investigating protein-ligand association pathways and their physical determinants across diverse molecular systems.
ER -