Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model

TL;DR

This paper introduces a novel implicit solvent model for proteins by distilling knowledge from a protein language model into a graph neural network, enabling accurate, transferable, and efficient protein simulations including folding and disordered states.

Contribution

The study presents a new data-driven implicit solvent model that combines protein language models with graph neural networks, overcoming traditional limitations of ISMs.

Findings

The GNN potential enables stable, long-timescale molecular dynamics simulations.

The hybrid model accurately reproduces protein folding free-energy landscapes.

The approach predicts structural ensembles of intrinsically disordered proteins.

Abstract

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools.