Score-based Enhanced Sampling for Protein Molecular Dynamics
Keywords: molecular dynamics, generative modeling, conformational sampling, diffusion models, score-based models, proteins
TL;DR: Repurpose score-based generative models for protein conformation sampling in an enhanced sampling manner.
Abstract: The dynamic nature of proteins is crucial for determining their biological functions and properties, and molecular dynamics (MD) simulations stand as a predominant tool to study such phenomena. By utilizing empirically derived force fields, MD simulations explore the conformational space through numerically evolving the system along MD trajectories. However, the high-energy barrier of the force fields can hamper the exploration of MD, resulting in inadequately sampled ensemble. In this paper, we propose leveraging score-based generative models (SGMs) trained on large-scale general protein structures to perform protein con- formational sampling to complement traditional MD simulations. Experimental results demonstrate the effectiveness of our approach on several benchmark systems by comparing the results with long MD trajectories and state-of-the-art generative structure prediction models.
Submission Number: 28
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