Aligning Proteins and Language: A Foundation Model for Protein Retrieval
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913886869913600 |
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| author | Wu, Qifeng Liu, Zhengzhe Zhu, Han Zhao, Yizhou Kihara, Daisuke Xu, Min |
| author_facet | Wu, Qifeng Liu, Zhengzhe Zhu, Han Zhao, Yizhou Kihara, Daisuke Xu, Min |
| contents | This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derived by structural determination methods like cryo-Electron Microscopy (cryo-EM). Motivated by the recent progress of vision-language models (VLMs), we propose a CLIP-style framework for aligning 3D protein structures with functional annotations using contrastive learning. For model training, we propose a large-scale dataset of approximately 200,000 protein-caption pairs with rich functional descriptors. We evaluate our model in both in-domain and more challenging cross-database retrieval on Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) dataset, respectively. In both cases, our approach demonstrates promising zero-shot retrieval performance, highlighting the potential of multimodal foundation models for structure-function understanding in protein biology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aligning Proteins and Language: A Foundation Model for Protein Retrieval Wu, Qifeng Liu, Zhengzhe Zhu, Han Zhao, Yizhou Kihara, Daisuke Xu, Min Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derived by structural determination methods like cryo-Electron Microscopy (cryo-EM). Motivated by the recent progress of vision-language models (VLMs), we propose a CLIP-style framework for aligning 3D protein structures with functional annotations using contrastive learning. For model training, we propose a large-scale dataset of approximately 200,000 protein-caption pairs with rich functional descriptors. We evaluate our model in both in-domain and more challenging cross-database retrieval on Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) dataset, respectively. In both cases, our approach demonstrates promising zero-shot retrieval performance, highlighting the potential of multimodal foundation models for structure-function understanding in protein biology. |
| title | Aligning Proteins and Language: A Foundation Model for Protein Retrieval |
| topic | Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.08023 |