# A TRIM Family-Based Strategy for TRIMCIV Target Prediction in a Pan-Cancer Context with Multi-Omics Data and Protein Docking Integration

## TL;DR

This paper introduces a new tool called TRIMCIVtargeter that predicts cancer-related protein interactions for the TRIM CIV subfamily using multi-omics data and structural features.

## Contribution

The novel contribution is a cancer-specific, data-driven method for TRIMCIV target prediction that avoids artificial assumptions and integrates multi-omics and docking data.

## Key findings

- TRIMCIV proteins show consistent correlation with differentially expressed genes in cancers.
- TRIMCIVtargeter achieves robust performance using SVM-based models trained on 718 experimentally validated TRIM–target pairs.
- The tool provides a new framework for family-specific PPI prediction in oncology.

## Abstract

The E3 TRIM family plays a key role in cancer, but identifying which proteins they interact with is costly and challenging. Current prediction methods struggle because they rely on incomplete data or overlook how these interactions change in diseases like cancer. To solve this issue, we studied the largest TRIM subfamily (CIV), collecting hundreds of their known interactions from past experiments. We observed that CIV proteins consistently correlate with DEGs in cancers, unlike other TRIM members. Using this pattern—along with structural features and cancer-specific data—we built a computational tool called TRIMCIVtargeter to predict new CIV targets. Notably, our approach avoids artificial assumptions and accounts for disease context. This tool provides researchers with a faster, more accurate way to uncover TRIM-related cancer mechanisms, potentially accelerating the discovery of new therapeutic targets. By focusing on real-world biological trends, TRIMCIVtargeter advances our understanding of how these proteins contribute to cancer and offers a framework for studying other protein families in disease.

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.