Dynamic insights into the structural evolution of ACE2–RBD interactions through molecular dynamics simulation, Markov state modeling, and large language model mutation prediction

The viral Spike protein of SARS-CoV-2 is responsible for virus-host recognition, in which the receptor-binding domain (RBD) binds to the host receptor angiotensin-converting enzyme 2 (ACE2). Structural biology studies have revealed the structures and binding mechanisms of several variants of concern, but they inherently lack dynamic information. Previous molecular dynamics (MD) simulations have generated vast trajectories, but the metastable states are insufficiently detected and analyzed. In this study, we dissect extensive MD simulations of the ACE2-RBD complex of the wild type and three pivotal Omicron variants: BA.2, BA.2.86, and JN.1. Employing the Time-lagged independent component analysis-Markov State Models method, we identify four principal metastable states for each system and demonstrate that lineage-defining mutations systematically alter the thermodynamic properties, such as equilibrium populations and interaction energies, as well as their transition kinetics. Our study establishes a robust paradigm for extracting detailed thermodynamic and kinetic insights from MD simulations that surpass conventional analysis. Furthermore, by scanning point mutations on the RBD, evaluating these mutations with a fine-tuned large language model, and investigating the resulting structural interactions at the binding interface, our work provides a dynamic, atomic-level view of the structural evolution of ACE2-RBD interactions, offering a mechanistic basis to aid in future viral surveillance and therapeutic design.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2025-11-21
DOI
https://doi.org/10.1063/5.0300029
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
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article

Dynamic insights into the structural evolution of ACE2–RBD interactions through molecular dynamics simulation, Markov state modeling, and large language model mutation prediction

Tong Wang, Yutao Zhou
The Journal of Chemical Physics
SARS-CoV-2 and COVID-19 Research
article

Dynamic insights into the structural evolution of ACE2–RBD interactions through molecular dynamics simulation, Markov state modeling, and large language model mutation prediction

Tong Wang, Yutao Zhou
article en

Abstract

The viral Spike protein of SARS-CoV-2 is responsible for virus-host recognition, in which the receptor-binding domain (RBD) binds to the host receptor angiotensin-converting enzyme 2 (ACE2). Structural biology studies have revealed the structures and binding mechanisms of several variants of concern, but they inherently lack dynamic information. Previous molecular dynamics (MD) simulations have generated vast trajectories, but the metastable states are insufficiently detected and analyzed. In this study, we dissect extensive MD simulations of the ACE2-RBD complex of the wild type and three pivotal Omicron variants: BA.2, BA.2.86, and JN.1. Employing the Time-lagged independent component analysis-Markov State Models method, we identify four principal metastable states for each system and demonstrate that lineage-defining mutations systematically alter the thermodynamic properties, such as equilibrium populations and interaction energies, as well as their transition kinetics. Our study establishes a robust paradigm for extracting detailed thermodynamic and kinetic insights from MD simulations that surpass conventional analysis. Furthermore, by scanning point mutations on the RBD, evaluating these mutations with a fine-tuned large language model, and investigating the resulting structural interactions at the binding interface, our work provides a dynamic, atomic-level view of the structural evolution of ACE2-RBD interactions, offering a mechanistic basis to aid in future viral surveillance and therapeutic design.

The Journal of Chemical PhysicsVol. 163(19)
Peking University (CN), Center for Life Sciences (CN)
Openalex Percentile: Top 58%
SARS-CoV-2 and COVID-19 Research
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