OPTAR: a computational tool for target discovery based on disease correlation inference from literature of interacting proteins

The identification of novel therapeutic targets remains a major bottleneck in target-based drug discovery, particularly when mining large-scale omics data. Although transcriptomic and proteomic profiling generate extensive lists of disease-associated candidates, prioritizing truly novel and druggable targets, especially those lacking active compounds, requires the assistance of advanced computational strategies. Here, we report the development of a new computational tool named OPTAR (Omics and Pocket Analysis-based Target Assessment and Ranking) for identifying promising new target proteins from omics data. These new target proteins are expected to have no active compounds, and have not previously been reported to be correlated to the disease of interest. OPTAR applies a multi-layer filtering and ranking workflow, including automated literature-based exclusion of known disease-associated proteins, drug availability screening, and algorithm-driven disease correlation inference, enabling systematic de novo target discovery. From the hepatocellular carcinoma (HCC) omics data used by the previously reported tool OTTM (Omics and Text-driven Translational Medicine), OPTAR identified high-ranking candidate proteins at the intersection of 'hepatocellular carcinoma' and 'cell cycle' lists. Functional verification indicated that silencing of UBE2J1, KDELR3, and VTI1A in HCC cells inhibited cell viability and reduced the migration and invasion abilities of HCC cells. Furthermore, it was found that UBE2J1 was upregulated in HCC tissues, and its knockdown induced apoptosis-related changes, and cell cycle disorder. Together, these findings establish OPTAR as a reliable and efficient computational tool for promising therapeutic target discovery with high originality from omics data.