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A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein
Yisha Huang1, Jiajia Xuan1, Jiayan Liang1
1MOE 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, China.
Abstract:
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.
Insights
TRIM CIV targets were identified using TRIMCIVtargeter, a novel computational tool. This approach integrates multi-dimensional features for precise cancer-specific protein-protein interaction prediction, aiding oncology research.
Area of Science:
- Molecular Biology
- Bioinformatics
- Oncology
Background:
- The TRIM CIV subfamily is crucial in cancer progression via ubiquitination.
- Identifying TRIM CIV substrates is key to understanding tumorigenesis.
- Existing protein-protein interaction (PPI) prediction models lack cancer specificity and robust negative datasets.
Purpose of the Study:
- To develop a precise method for identifying TRIM CIV targets.
- To create a cancer-specific PPI prediction model for the TRIM CIV subfamily.
Main Methods:
- Developed TRIMCIVtargeter, integrating multi-dimensional PPI features: cancer-specific expression differences/correlations, docking scores, and context.
- Trained two SVM-based binary models using proteomic and transcriptomic data from 718 validated TRIM-target pairs.
- Utilized a fair feature space, avoiding hypothetical non-interacting pairs for robust predictions.
Main Results:
- Achieved robust prediction performance for TRIM CIV targets in various cancers.
- The models successfully integrated diverse features for accurate PPI prediction.
- TRIMCIVtargeter demonstrated effectiveness by circumventing hypothetical non-interacting pairs.
Conclusions:
- TRIMCIVtargeter provides a valuable resource for studying TRIM CIV functions in cancer.
- Offers a novel perspective for family-specific PPI prediction in oncology.
- Has significant implications for biomarker discovery and therapeutic targeting in cancer.
