PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks

Proteins move and deform to ensure their biological functions.

Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem.

This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations.

We develop a task-specific loss function enforcing data symmetries, including scaling and permutation operations.

Our method PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) leverages transfer learning from pre-trained protein language models through an SE(3)-equivariant graph neural network.

When trained and evaluated on the Protein Data Bank, PETIMOT shows superior performance in time and accuracy, capturing protein dynamics, particularly large/slow conformational changes, compared to state-of-the-art diffusion and flow-matching approaches, as well as traditional physics-based models.

Our code and protocols are available at this https URL.