Learning Topological Representations for Molecular Dynamics

2025-04-01 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Life Sciences & Biology, Mathematics & Computational Sciences, Engineering & Applied Sciences · Depth: Expert, extended

Summary

Molecular dynamics (MD) simulations generate high-dimensional trajectories, requiring effective molecular descriptors for analysis. This research introduces the masked Flood complex, a protein-tailored modification of a simplicial complex construction, to enhance persistent homology (PH) as a general-purpose representation for MD. This method yields information-rich, geometry-aware summaries of protein conformations, evaluated on protein class prediction, frame-level observable regression, and Markov state model (MSM) estimation. Results on the mdCATH dataset, comprising 5,398 domains with trajectories up to 500 ns at 450 K, demonstrate that PH-based descriptors are competitive, with masked Flood PH showing the most consistent overall performance. Furthermore, integrating topologically-informed MSMs into the MarS-FM generative modeling framework consistently produces better ensemble statistics than MSMs based on physical observables, and the model exhibits promising transferability to fast folding proteins simulated at 350 K.

Key takeaway

Research Scientists developing molecular dynamics analysis tools, you should consider persistent homology, specifically the masked Flood complex, as it consistently outperforms traditional methods in capturing structural and dynamic information. Integrate these topological representations into your Markov State Models to improve generative modeling and ensemble statistics, especially for diverse protein datasets like mdCATH or fast folding proteins. This approach offers a robust and broadly informative representation for complex molecular systems.

Key insights

Masked Flood complex enhances persistent homology for molecular dynamics by incorporating protein-specific structural bias.

Principles

• Domain knowledge improves persistent homology computation.
• Shared embedding space aids population-level learning.
• Topological features resolve dynamically relevant processes.

Method

Map atomic configurations to vectorized topological summaries using the masked Flood complex, then learn kinetic embeddings for Markov State Model estimation.

In practice

• Use masked Flood PH for protein conformation analysis.
• Integrate topologically-informed MSMs into generative models.
• Apply PH-based descriptors for protein class prediction.

Topics

• Molecular Dynamics
• Persistent Homology
• Masked Flood Complex
• Protein Conformations
• Markov State Models
• Generative AI

Code references

• compsciencelab/mdCATH
• torchmd/torchmd-protein-thermodynamics
• google-deepmind/alphafold

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.