An All-Atom Generative Model for Designing Protein Complexes
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916938598318080 |
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| author | Chen, Ruizhe Xue, Dongyu Zhou, Xiangxin Zheng, Zaixiang Zeng, Xiangxiang Gu, Quanquan |
| author_facet | Chen, Ruizhe Xue, Dongyu Zhou, Xiangxin Zheng, Zaixiang Zeng, Xiangxiang Gu, Quanquan |
| contents | Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An All-Atom Generative Model for Designing Protein Complexes Chen, Ruizhe Xue, Dongyu Zhou, Xiangxin Zheng, Zaixiang Zeng, Xiangxiang Gu, Quanquan Machine Learning Proteins typically exist in complexes, interacting with other proteins or biomolecules to perform their specific biological roles. Research on single-chain protein modeling has been extensively and deeply explored, with advancements seen in models like the series of ESM and AlphaFold2. Despite these developments, the study and modeling of multi-chain proteins remain largely uncharted, though they are vital for understanding biological functions. Recognizing the importance of these interactions, we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch. It also performs folding and inverse-folding tasks for multi-chain proteins. Moreover, APM demonstrates versatility in downstream applications: it achieves enhanced performance through supervised fine-tuning (SFT) while also supporting zero-shot sampling in certain tasks, achieving state-of-the-art results. We released our code at https://github.com/bytedance/apm. |
| title | An All-Atom Generative Model for Designing Protein Complexes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.13075 |