MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types
Abstract Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202410981 |
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| _version_ | 1849471687387512832 |
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| author | Juan Xie Ruihan Dong Jintao Zhu Haoyu Lin Shiwei Wang Luhua Lai |
| author_facet | Juan Xie Ruihan Dong Jintao Zhu Haoyu Lin Shiwei Wang Luhua Lai |
| author_sort | Juan Xie |
| collection | DOAJ |
| description | Abstract Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi‐modal deep learning framework, is developed to predict functional phosphorylation sites. MMFuncPhos outperforms existing functional phosphorylation site prediction approaches. EFuncType is further developed based on transfer learning to predict whether phosphorylation of a residue upregulates or downregulates enzyme activity for the first time. The functional phosphorylation sites predicted by MMFuncPhos and the regulatory types predicted by EFuncType align with experimental findings from several newly reported protein phosphorylation studies. The study contributes to the understanding of the functional regulatory mechanism of phosphorylation and provides valuable tools for precision medicine, enzyme engineering, and drug discovery. For user convenience, these two prediction models are integrated into a web server which can be accessed at http://pkumdl.cn:8000/mmfuncphos. |
| format | Article |
| id | doaj-art-387fc25f9ee24e8eb75a7633fe28fff8 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-387fc25f9ee24e8eb75a7633fe28fff82025-08-20T03:24:44ZengWileyAdvanced Science2198-38442025-03-01129n/an/a10.1002/advs.202410981MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory TypesJuan Xie0Ruihan Dong1Jintao Zhu2Haoyu Lin3Shiwei Wang4Luhua Lai5Center for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 ChinaPTN Graduate Program Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 ChinaCenter for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 ChinaPeking‐Tsinghua Center for Life Sciences at BNLMS College of Chemistry and Molecular Engineering Peking University Beijing 100871 ChinaPeking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies Chengdu Sichuan 610213 ChinaCenter for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 ChinaAbstract Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi‐modal deep learning framework, is developed to predict functional phosphorylation sites. MMFuncPhos outperforms existing functional phosphorylation site prediction approaches. EFuncType is further developed based on transfer learning to predict whether phosphorylation of a residue upregulates or downregulates enzyme activity for the first time. The functional phosphorylation sites predicted by MMFuncPhos and the regulatory types predicted by EFuncType align with experimental findings from several newly reported protein phosphorylation studies. The study contributes to the understanding of the functional regulatory mechanism of phosphorylation and provides valuable tools for precision medicine, enzyme engineering, and drug discovery. For user convenience, these two prediction models are integrated into a web server which can be accessed at http://pkumdl.cn:8000/mmfuncphos.https://doi.org/10.1002/advs.202410981drug discoveryenzyme engineeringfunctional phosphorylation sitesmulti‐modal deep learning frameworkphosphorylation regulation typesprecision medicine |
| spellingShingle | Juan Xie Ruihan Dong Jintao Zhu Haoyu Lin Shiwei Wang Luhua Lai MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types Advanced Science drug discovery enzyme engineering functional phosphorylation sites multi‐modal deep learning framework phosphorylation regulation types precision medicine |
| title | MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types |
| title_full | MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types |
| title_fullStr | MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types |
| title_full_unstemmed | MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types |
| title_short | MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types |
| title_sort | mmfuncphos a multi modal learning framework for identifying functional phosphorylation sites and their regulatory types |
| topic | drug discovery enzyme engineering functional phosphorylation sites multi‐modal deep learning framework phosphorylation regulation types precision medicine |
| url | https://doi.org/10.1002/advs.202410981 |
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