Summary of Protdat: a Unified Framework For Protein Sequence Design From Any Protein Text Description, by Xiao-yu Guo et al.
ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description
by Xiao-Yu Guo, Yi-Fan Li, Yuan Liu, Xiaoyong Pan, Hong-Bin Shen
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed ProtDAT framework is a novel de novo method for designing proteins from descriptive text inputs. Building upon the inherent characteristics of protein data, it unifies sequences and text as a cohesive whole, integrating protein sequences and textual information through a multi-modal cross-attention mechanism. Experimental results demonstrate that ProtDAT achieves state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProtDAT is a new way to design proteins using words. It takes text descriptions of proteins and generates sequences that are similar to the original ones. This helps scientists develop new proteins with specific functions, which can be useful for making medicines or engineering enzymes. The approach is better than previous methods at generating realistic protein sequences. |
Keywords
» Artificial intelligence » Cross attention » Multi modal
