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

Abstract

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 systematicde 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.

Acknowledgments

We gratefully acknowledge the financial supports from the National Key R&D Program of China (NO. 2022YFC3400500), the Strategic Priority Research Program of the Chinese Academy of Sciences (NO. XDB0830301), the National Natural Science Foundation of China (NO. 81903538), the Shanghai Municipal Health Commission Medical New Technology Project (NO. 2025ZZ2060), Shanghai Municipal Education Commission AI for Science Project (NO. 301-0406), Shanghai Oriental Talent Plan Youth Project (NO. QNJY2025170), and Science and Technology Department of Guizhou Province (grant number [2024]015).

Author contributions

The authors confirm their contributions to the work as follows: cell-based experiments and manuscript writing: Yuan X; figure preparation: Zhou S; compound screening: Yu J; reference collection and organization: Wang M; manuscript checking and revision: Luo C; development of the computational tool and the writing of the corresponding methodological and related content: Zhang H. All authors reviewed the results and approved the final version of the manuscript.

Data availability

The data generated or analyzed during this study are included in this published article and its supplementary information files. Additional data related to this study are available from the corresponding author upon reasonable request.

Conflict of interest

The authors declare that they have no conflict of interest.

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