EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning mo...
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Main Authors | , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
12.10.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Mapping the conformational dynamics of proteins is crucial for elucidating
their functional mechanisms. While Molecular Dynamics (MD) simulation enables
detailed time evolution of protein motion, its computational toll hinders its
use in practice. To address this challenge, multiple deep learning models for
reproducing and accelerating MD have been proposed drawing on transport-based
generative methods. However, existing work focuses on generation through
transport of samples from prior distributions, that can often be distant from
the data manifold. The recently proposed framework of stochastic interpolants,
instead, enables transport between arbitrary distribution endpoints. Building
upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model
that bridges all-atom protein dynamics simulation time steps directly. Our
approach unifies diverse sampling methods and is benchmarked against existing
models on trajectory data of fast folding proteins. EquiJump achieves
state-of-the-art results on dynamics simulation with a transferable model on
all of the fast folding proteins. |
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DOI: | 10.48550/arxiv.2410.09667 |