Learning the PTM Code through a Coarse-to-Fine, Mechanism-Aware Framework
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912672680771584 |
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| author | Zhang, Jingjie Cao, Hanqun Gao, Zijun Wang, Yu Li, Shaoning Xu, Jun Tan, Cheng Zhu, Jun Hsieh, Chang-Yu Gu, Chunbin Heng, Pheng Ann |
| author_facet | Zhang, Jingjie Cao, Hanqun Gao, Zijun Wang, Yu Li, Shaoning Xu, Jun Tan, Cheng Zhu, Jun Hsieh, Chang-Yu Gu, Chunbin Heng, Pheng Ann |
| contents | Post-translational modifications (PTMs) form a combinatorial "code" that regulates protein function, yet deciphering this code - linking modified sites to their catalytic enzymes - remains a central unsolved problem in understanding cellular signaling and disease. We introduce COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies residue-level PTM profiling with enzyme-substrate assignment. COMPASS-PTM integrates evolutionary representations from protein language models with physicochemical priors and a crosstalk-aware prompting mechanism that explicitly models inter-PTM dependencies. This design allows the model to learn biologically coherent patterns of cooperative and antagonistic modifications while addressing the dual long-tail distribution of PTM data. Across multiple proteome-scale benchmarks, COMPASS-PTM establishes new state-of-the-art performance, including a 122% relative F1 improvement in multi-label site prediction and a 54% gain in zero-shot enzyme assignment. Beyond accuracy, the model demonstrates interpretable generalization, recovering canonical kinase motifs and predicting disease-associated PTM rewiring caused by missense variants. By bridging statistical learning with biochemical mechanism, COMPASS-PTM unifies site-level and enzyme-level prediction into a single framework that learns the grammar underlying protein regulation and signaling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning the PTM Code through a Coarse-to-Fine, Mechanism-Aware Framework Zhang, Jingjie Cao, Hanqun Gao, Zijun Wang, Yu Li, Shaoning Xu, Jun Tan, Cheng Zhu, Jun Hsieh, Chang-Yu Gu, Chunbin Heng, Pheng Ann Computational Engineering, Finance, and Science Post-translational modifications (PTMs) form a combinatorial "code" that regulates protein function, yet deciphering this code - linking modified sites to their catalytic enzymes - remains a central unsolved problem in understanding cellular signaling and disease. We introduce COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies residue-level PTM profiling with enzyme-substrate assignment. COMPASS-PTM integrates evolutionary representations from protein language models with physicochemical priors and a crosstalk-aware prompting mechanism that explicitly models inter-PTM dependencies. This design allows the model to learn biologically coherent patterns of cooperative and antagonistic modifications while addressing the dual long-tail distribution of PTM data. Across multiple proteome-scale benchmarks, COMPASS-PTM establishes new state-of-the-art performance, including a 122% relative F1 improvement in multi-label site prediction and a 54% gain in zero-shot enzyme assignment. Beyond accuracy, the model demonstrates interpretable generalization, recovering canonical kinase motifs and predicting disease-associated PTM rewiring caused by missense variants. By bridging statistical learning with biochemical mechanism, COMPASS-PTM unifies site-level and enzyme-level prediction into a single framework that learns the grammar underlying protein regulation and signaling. |
| title | Learning the PTM Code through a Coarse-to-Fine, Mechanism-Aware Framework |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.23492 |