Bi-TEAM: A Unified Cross-Scale Representation Learning Framework for Chemically Modified Biomolecules
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910037973139456 |
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| author | Gu, Chunbin Gao, Zijun He, Mutian Zhang, Jingjie Wen, Haipeng Luo, Zihao Wang, Xiaorui Cao, Hanqun Bu, Jiajun Hsieh, Chang-Yu Heng, Pheng Ann |
| author_facet | Gu, Chunbin Gao, Zijun He, Mutian Zhang, Jingjie Wen, Haipeng Luo, Zihao Wang, Xiaorui Cao, Hanqun Bu, Jiajun Hsieh, Chang-Yu Heng, Pheng Ann |
| contents | Representation learning for protein biochemical space faces a difficult trade-off: protein language models excel at capturing long-range biological semantics but often miss fine-grained chemical details. Conversely, chemical language models encode atomic information but lack broader sequence context. To address this, we introduce Bi-TEAM (Bi-gated Residual Space Modification), a general framework that injects localized chemical variation into global protein contexts. By ensuring robustness against perturbations such as non-canonical amino acids, post-translational modifications (PTMs), and topological constraints, Bi-TEAM uncovers functional chemical dependencies often missed by evolutionary baselines. Mechanistically, Bi-TEAM maps non-canonical residues to their natural counterparts and injects atomic-level data via a bi-gated residual fusion mechanism. Crucially, this process uses modification-aware prompts to ensure that local structural changes influence global functional representations without requiring alphabet expansion. We evaluated Bi-TEAM on ten datasets spanning chemically modified peptides, PTMs, and natural proteins. The model consistently outperformed state-of-the-art baselines, achieving up to a 66 percent improvement in Matthews correlation coefficient (MCC) on scaffold-similarity splits and a 350 percent increase in hemolysis prediction accuracy. Furthermore, when deployed as an oracle for generative modeling, Bi-TEAM nearly quadrupled the success rate for designing cell-penetrating cyclic peptides. By unifying biological semantics with chemical precision, Bi-TEAM provides a versatile foundation for machine learning driven exploration of peptide and protein biochemical space. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_01873 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bi-TEAM: A Unified Cross-Scale Representation Learning Framework for Chemically Modified Biomolecules Gu, Chunbin Gao, Zijun He, Mutian Zhang, Jingjie Wen, Haipeng Luo, Zihao Wang, Xiaorui Cao, Hanqun Bu, Jiajun Hsieh, Chang-Yu Heng, Pheng Ann Biomolecules Representation learning for protein biochemical space faces a difficult trade-off: protein language models excel at capturing long-range biological semantics but often miss fine-grained chemical details. Conversely, chemical language models encode atomic information but lack broader sequence context. To address this, we introduce Bi-TEAM (Bi-gated Residual Space Modification), a general framework that injects localized chemical variation into global protein contexts. By ensuring robustness against perturbations such as non-canonical amino acids, post-translational modifications (PTMs), and topological constraints, Bi-TEAM uncovers functional chemical dependencies often missed by evolutionary baselines. Mechanistically, Bi-TEAM maps non-canonical residues to their natural counterparts and injects atomic-level data via a bi-gated residual fusion mechanism. Crucially, this process uses modification-aware prompts to ensure that local structural changes influence global functional representations without requiring alphabet expansion. We evaluated Bi-TEAM on ten datasets spanning chemically modified peptides, PTMs, and natural proteins. The model consistently outperformed state-of-the-art baselines, achieving up to a 66 percent improvement in Matthews correlation coefficient (MCC) on scaffold-similarity splits and a 350 percent increase in hemolysis prediction accuracy. Furthermore, when deployed as an oracle for generative modeling, Bi-TEAM nearly quadrupled the success rate for designing cell-penetrating cyclic peptides. By unifying biological semantics with chemical precision, Bi-TEAM provides a versatile foundation for machine learning driven exploration of peptide and protein biochemical space. |
| title | Bi-TEAM: A Unified Cross-Scale Representation Learning Framework for Chemically Modified Biomolecules |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2603.01873 |