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Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

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Related Experiment Video

Updated: Jan 7, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics.

Shengchao Liu¹, Weitao Du², Hannan Xu³

¹University of California Berkeley, Berkeley, CA, US. shengchao.liu@berkeley.edu.

Nature Communications

|December 30, 2025

Summary

NeuralMD enhances molecular dynamics (MD) simulations for drug discovery. This machine learning (ML) tool improves protein-ligand binding predictions by accurately modeling complex dynamics.

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Last Updated: Jan 7, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

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Published on: April 13, 2022

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Published on: September 13, 2014

Area of Science:

Computational chemistry
Biophysics
Drug discovery

Background:

Molecular dynamics (MD) simulations are crucial for drug discovery, aiding in predicting protein-ligand binding affinities and dynamics.
Existing numerical and machine learning (ML) methods have improved MD efficiency, but modeling long-timescale dynamics remains a significant challenge.

Purpose of the Study:

To introduce NeuralMD, a novel ML surrogate designed to accelerate and enhance MD simulations for protein-ligand binding.
To address the limitations of current methods in accurately capturing complex molecular dynamics.

Main Methods:

NeuralMD utilizes a physics-informed, multi-grained, group-symmetric framework.
It comprises BindingNet for multi-level protein-ligand interaction analysis and an augmented neural differential equation solver for learning Newtonian trajectories.

Main Results:

NeuralMD demonstrated up to 15x lower reconstruction error and 70% higher validity compared to existing ML baselines across multiple simulation tasks.
Predicted molecular oscillations closely matched ground-truth dynamics, indicating high accuracy.

Conclusions:

NeuralMD significantly advances the accuracy and efficiency of MD simulations in drug discovery.
It provides a robust foundation for future research in protein-ligand interaction modeling and simulation.