Summary of Taxdiff: Taxonomic-guided Diffusion Model For Protein Sequence Generation, by Lin Zongying et al.
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
by Lin Zongying, Li Hao, Lv Liuzhenghao, Lin Bin, Zhang Junwu, Chen Calvin Yu-Chian, Yuan Li, Tian Yonghong
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes TaxDiff, a taxonomic-guided diffusion model for controllable protein sequence generation. The model combines biological species information with generative capabilities to generate structurally stable proteins within the sequence space. TaxDiff inserts taxonomic control information into each layer of the transformer block, achieving fine-grained control and ensuring sequence consistency and structural foldability. Experimental results demonstrate that TaxDiff outperforms other models in protein sequence generation benchmarks, both in controllable and unconditional generation. Additionally, the generated sequences surpass those produced by direct-structure-generation models in terms of confidence based on predicted structures, requiring only a quarter of the time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TaxDiff is a new way to design proteins with specific functions and structural stability. Scientists already have tools that can generate protein sequences, but these tools are limited because they don’t let you control what kind of sequence you get. TaxDiff fixes this by using information about different species and biological processes to guide the generation of protein sequences. This means you can make sure the proteins you design are stable and work correctly. The researchers tested TaxDiff on several tasks and found that it outperformed other models in generating protein sequences. |
Keywords
* Artificial intelligence * Diffusion model * Transformer
