A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration
The TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorige...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Biology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-7737/14/7/742 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849246762801299456 |
|---|---|
| author | Yisha Huang Jiajia Xuan Jiayan Liang Xixi Liu Yonglei Luo Xuejuan Gao Wanting Liu |
| author_facet | Yisha Huang Jiajia Xuan Jiayan Liang Xixi Liu Yonglei Luo Xuejuan Gao Wanting Liu |
| author_sort | Yisha Huang |
| collection | DOAJ |
| description | The TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorigenesis. Current protein–protein interaction (PPI) prediction models face challenges, including an inherent deficiency of negative datasets, biased feature integration, and the absence of a cancer-specific interaction context. To achieve the precise identification of TRIMCIV targets, we developed TRIMCIVtargeter with predictive models that systematically integrates multi-dimensional PPI features—expression differences and correlations in specific cancer, comparable protein-docking scores, and cancer-specific context. Learning from the functional and structural interaction features between 718 experimentally validated TRIM–target pairs, two types of SVM-based binary models were independently trained using proteomic and transcriptomic data. Our models achieved robust prediction performance in cancers utilizing a fair feature space and circumventing hypothetical non-interacting pairs. TRIMCIVtargeter not only provides a cancer-related resource for studying TRIMCIV-mediated regulatory mechanisms but also offers a new perspective for family-specific PPI prediction, holding significant implications for biomarker discovery and therapeutic targeting in oncology. The online platform of TRIMCIVtargeter is now available. |
| format | Article |
| id | doaj-art-095b7f67e3944c048e16d29b5d7e0528 |
| institution | Kabale University |
| issn | 2079-7737 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj-art-095b7f67e3944c048e16d29b5d7e05282025-08-20T03:58:25ZengMDPI AGBiology2079-77372025-06-0114774210.3390/biology14070742A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking IntegrationYisha Huang0Jiajia Xuan1Jiayan Liang2Xixi Liu3Yonglei Luo4Xuejuan Gao5Wanting Liu6MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, ChinaThe TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorigenesis. Current protein–protein interaction (PPI) prediction models face challenges, including an inherent deficiency of negative datasets, biased feature integration, and the absence of a cancer-specific interaction context. To achieve the precise identification of TRIMCIV targets, we developed TRIMCIVtargeter with predictive models that systematically integrates multi-dimensional PPI features—expression differences and correlations in specific cancer, comparable protein-docking scores, and cancer-specific context. Learning from the functional and structural interaction features between 718 experimentally validated TRIM–target pairs, two types of SVM-based binary models were independently trained using proteomic and transcriptomic data. Our models achieved robust prediction performance in cancers utilizing a fair feature space and circumventing hypothetical non-interacting pairs. TRIMCIVtargeter not only provides a cancer-related resource for studying TRIMCIV-mediated regulatory mechanisms but also offers a new perspective for family-specific PPI prediction, holding significant implications for biomarker discovery and therapeutic targeting in oncology. The online platform of TRIMCIVtargeter is now available.https://www.mdpi.com/2079-7737/14/7/742TRIM familyPPI predictioncancermulti-omicsmachine learningprotein docking |
| spellingShingle | Yisha Huang Jiajia Xuan Jiayan Liang Xixi Liu Yonglei Luo Xuejuan Gao Wanting Liu A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration Biology TRIM family PPI prediction cancer multi-omics machine learning protein docking |
| title | A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration |
| title_full | A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration |
| title_fullStr | A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration |
| title_full_unstemmed | A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration |
| title_short | A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration |
| title_sort | trim family based strategy for trimciv target prediction in a pan cancer context with multi omics data and protein docking integration |
| topic | TRIM family PPI prediction cancer multi-omics machine learning protein docking |
| url | https://www.mdpi.com/2079-7737/14/7/742 |
| work_keys_str_mv | AT yishahuang atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT jiajiaxuan atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT jiayanliang atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT xixiliu atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT yongleiluo atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT xuejuangao atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT wantingliu atrimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT yishahuang trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT jiajiaxuan trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT jiayanliang trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT xixiliu trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT yongleiluo trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT xuejuangao trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration AT wantingliu trimfamilybasedstrategyfortrimcivtargetpredictioninapancancercontextwithmultiomicsdataandproteindockingintegration |