PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
arXiv:2504.02839v2 Announce Type: replace-cross Abstract: 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 (P
The increasing sophistication of AI models, particularly in graph neural networks, is enabling breakthroughs in complex biological problems that were previously intractable with sparse data.
This research provides a novel method for understanding protein dynamics, which is fundamental to drug discovery, enzyme engineering, and synthetic biology, accelerating progress in these critical fields.
The ability to infer protein motions from sparse experimental data changes how researchers can model complex biological systems, potentially reducing the need for extensive and costly empirical data collection.
- · Biopharmaceutical companies
- · Synthetic biology researchers
- · AI-driven drug discovery platforms
- · Computational biology sector
- · Traditional protein modeling approaches
- · Companies reliant solely on extensive empirical protein data
More accurate and faster identification of therapeutic targets and drug candidates.
Reduced R&D costs and shortened timelines for developing new drugs and biological materials.
Accelerated development of novel enzymes and proteins for industrial applications, impacting fields like biofuels and sustainable manufacturing.
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