AI & Life Sciences
Contact-Guided 3D Genome Structure Generation of E. coli via Diffusion Transformers
This study introduces a novel conditional diffusion-transformer framework for generating diverse ensembles of 3D Escherichia coli genome conformations, guided by Hi-C contact maps. Unlike previous methods that yield a single deterministic structure, this approach formulates genome reconstruction as a conditional generative modeling problem, sampling heterogeneous conformations consistent with input Hi-C data. By leveraging a synthetic dataset from molecular dynamics simulations and a transformer-based encoder with cross-attention, the model effectively reproduces Hi-C distance-decay and structural correlation while maintaining significant conformational diversity. This demonstrates the potential of diffusion-based generative modeling for ensemble-level 3D genome reconstruction, opening new avenues for understanding chromatin organization and its role in cellular processes.
Key Metrics for Success
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Mean SCC (CrossDiT-L)
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Mean Pairwise dRMSD (CrossDiT-L)
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Bond Length Multiplier (Structural Diversity)
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The proposed framework utilizes a conditional diffusion-transformer to generate 3D E. coli genome conformations. It employs a latent diffusion setting with a variational autoencoder (VAE) to encode 3D structures into a compressed latent space, ensuring per-bin alignment and replication-aware representations. Hi-C information is integrated via a transformer-based encoder and cross-attention, enforcing a one-way constraint from Hi-C to structure. The model is trained using a flow-matching objective for stable optimization.
Due to the scarcity of direct ground-truth 3D E. coli chromosome conformations, a synthetic dataset was generated using coarse-grained molecular dynamics (MD) simulations. This dataset includes chromatin ensembles and corresponding Hi-C maps under circular topology. Experimental Hi-C-derived interactions are incorporated as restraints to guide polymer dynamics, and structures are sampled under ongoing replication conditions (one to two chromosome copies via a replication factor G).
The generated structures successfully reproduce input Hi-C distance-decay (P(s) curves) and structural correlation metrics, like stratum-adjusted correlation coefficient (SCC), while maintaining substantial conformational diversity (measured by mean pairwise dRMSD). CrossDiT-L achieved a mean SCC of 0.962 and a mean pairwise dRMSD of 0.700, indicating high fidelity and diversity compared to a baseline of 0.072 dRMSD.
0.962
High SCC achieved by CrossDiT-L, showing strong agreement with Hi-C map structure.
Enterprise Process Flow
Input Hi-C Map
→
Hi-C Encoder
→
Latent Space Diffusion
→
Sequence Decoder
→
3D Genome Conformations
| Feature | CrossDiT-S (45M Params) | CrossDiT-L (634M Params) |
|---|---|---|
| Mean SCC | 0.824 | 0.962 |
| Mean Pairwise dRMSD | 0.666 | 0.700 |
| Structural Diversity (vs. Bond Length) | 1.84x | 1.94x |
| Computational Cost | Lower | Higher |
Impact of Ensemble Generation
Traditional methods for 3D genome reconstruction often yield a single consensus structure, overlooking the intrinsic heterogeneity of chromosome organization. This study's generative modeling approach produces diverse ensembles of 3D conformations, where ensemble-averaged contacts are consistent with the input Hi-C data. This directly addresses the biological reality of cellular variability and offers a more comprehensive understanding of genome dynamics beyond a static snapshot. This is crucial for studying dynamic processes like replication and transcription.
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Mean pairwise dRMSD for CrossDiT-L
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Baseline dRMSD (perturbed ensemble)
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Structural diversity (x bond length)
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