Latent Diffusion Models for Controllable RNA Sequence Generation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912054886006784 |
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| author | Huang, Kaixuan Yang, Yukang Fu, Kaidi Chu, Yanyi Cong, Le Wang, Mengdi |
| author_facet | Huang, Kaixuan Yang, Yukang Fu, Kaidi Chu, Yanyi Cong, Le Wang, Mengdi |
| contents | This work presents RNAdiffusion, a latent diffusion model for generating and optimizing discrete RNA sequences of variable lengths. RNA is a key intermediary between DNA and protein, exhibiting high sequence diversity and complex three-dimensional structures to support a wide range of functions. We utilize pretrained BERT-type models to encode raw RNA sequences into token-level, biologically meaningful representations. A Query Transformer is employed to compress such representations into a set of fixed-length latent vectors, with an autoregressive decoder trained to reconstruct RNA sequences from these latent variables. We then develop a continuous diffusion model within this latent space. To enable optimization, we integrate the gradients of reward models--surrogates for RNA functional properties--into the backward diffusion process, thereby generating RNAs with high reward scores. Empirical results confirm that RNAdiffusion generates non-coding RNAs that align with natural distributions across various biological metrics. Further, we fine-tune the diffusion model on mRNA 5' untranslated regions (5'-UTRs) and optimize sequences for high translation efficiencies. Our guided diffusion model effectively generates diverse 5'-UTRs with high Mean Ribosome Loading (MRL) and Translation Efficiency (TE), outperforming baselines in balancing rewards and structural stability trade-off. Our findings hold potential for advancing RNA sequence-function research and therapeutic RNA design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Latent Diffusion Models for Controllable RNA Sequence Generation Huang, Kaixuan Yang, Yukang Fu, Kaidi Chu, Yanyi Cong, Le Wang, Mengdi Machine Learning Artificial Intelligence Quantitative Methods This work presents RNAdiffusion, a latent diffusion model for generating and optimizing discrete RNA sequences of variable lengths. RNA is a key intermediary between DNA and protein, exhibiting high sequence diversity and complex three-dimensional structures to support a wide range of functions. We utilize pretrained BERT-type models to encode raw RNA sequences into token-level, biologically meaningful representations. A Query Transformer is employed to compress such representations into a set of fixed-length latent vectors, with an autoregressive decoder trained to reconstruct RNA sequences from these latent variables. We then develop a continuous diffusion model within this latent space. To enable optimization, we integrate the gradients of reward models--surrogates for RNA functional properties--into the backward diffusion process, thereby generating RNAs with high reward scores. Empirical results confirm that RNAdiffusion generates non-coding RNAs that align with natural distributions across various biological metrics. Further, we fine-tune the diffusion model on mRNA 5' untranslated regions (5'-UTRs) and optimize sequences for high translation efficiencies. Our guided diffusion model effectively generates diverse 5'-UTRs with high Mean Ribosome Loading (MRL) and Translation Efficiency (TE), outperforming baselines in balancing rewards and structural stability trade-off. Our findings hold potential for advancing RNA sequence-function research and therapeutic RNA design. |
| title | Latent Diffusion Models for Controllable RNA Sequence Generation |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2409.09828 |