Papers › RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models

RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models

17 Apr 2024arXiv:2404.11199archive 2025-07-28

Han Huang, Ziqian Lin, Dongchen He, Liang Hong, Yu Li

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the non-unique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in-silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

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interpolate_fn ml4bio/ribodiffusion/diffusion/noise_schedule.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 6a35b62fbc80f70a · report
expand_dims ml4bio/ribodiffusion/diffusion/noise_schedule.py official repository ran · our draft was wrong MIT (permissive) · e6110366588c5c65 · report
modulate ml4bio/ribodiffusion/models/transformer_layer.py official repository ran · honoured contract fingerprinted MIT (permissive) · 62fcb3912a967a50 · report
create_model ml4bio/ribodiffusion/models/utils.py official repository unverified MIT (permissive) · abfc536f6d116abc · report
expand_dims ml4bio/ribodiffusion/sampling.py official repository unverified MIT (permissive) · ce3c83921e12db4b · report
geo_batch ml4bio/ribodiffusion/models/GVP_diff.py official repository unverified MIT (permissive) · bdd8f1d250ba6e52 · report
get_data_inverse_scaler ml4bio/ribodiffusion/utils.py official repository unverified MIT (permissive) · faf4a5d59e3f19ad · report
get_data_scaler ml4bio/ribodiffusion/utils.py official repository unverified MIT (permissive) · f6477cfb5f1e7189 · report
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get_orientations_single ml4bio/ribodiffusion/datasets/utils.py official repository unverified MIT (permissive) · ecf2c47ec46adb5a · report
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get_rotation_frames ml4bio/ribodiffusion/models/esm_utils.py official repository unverified MIT (permissive) · dcd598af9c809427 · report
get_sampling_fn ml4bio/ribodiffusion/sampling.py official repository unverified MIT (permissive) · e7cc2a0ff746b735 · report
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tuple_index ml4bio/ribodiffusion/models/GVP_diff.py official repository unverified MIT (permissive) · de724c9930a6f2be · report

Tasks

Graph Neural Network

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Methods

Diffusion

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