Abstract 5127: Structure-guided discovery of potent and selective DGKα inhibitors for targeted cancer therapy

Abstract Introduction: Cancer remains a major cause of morbidity and mortality in North America. Diacylglycerol kinases (DGKs) are key regulators of lipid-mediated signaling, converting diacylglycerol (DAG) to phosphatidic acid (PA), two bioactive second messengers that orchestrate diverse cellular pathways. The DGK families comprise ten isoforms classified into five subtypes. Among these, DGKα—a type I isoform—plays critical roles in immune regulation, neuronal signaling, and membrane remodeling. In the context of cancer, DGKα enhances tumor cell proliferation and survival while suppressing cytotoxic T-cell and natural killer (NK) cell activity, thereby facilitating immune evasion. DGKα also promotes PD-L1 expression, further reinforcing tumor immune escape. Dysregulated DGKα signaling similarly contributes to immune dysfunction. To address these pathological roles, we aim to develop highly selective DGKα inhibitors by exploiting a distinct allosteric pocket to achieve precise and isoform-specific modulation of the enzyme. Methods: We generated high-confidence structural models of DGK isoforms including (α, β) using state-of-the-art AI prediction platforms, complemented by molecular dynamics (MD) simulations and binding free-energy methodologies. Structural models of DGKα and DGKβ were constructed in the presence of biologically relevant cofactors (ATP, Ca2+, Zn2+, Mg2+) to capture catalytically competent conformations. Each model underwent extensive validation through long-timescale MD simulations, and dominant conformational ensembles were extracted via clustering analyses. A generative-design pipeline was subsequently deployed to create and prioritize novel small-molecule inhibitors based on predicted potency, isoform selectivity, and physicochemical suitability. Top-ranked candidates were further interrogated through molecular docking, MD refinement, and free-energy calculations to characterize their binding poses. Results: The DGKα structural models demonstrated strong concordance with available in vitro data, supporting their suitability for downstream computational analyses. Molecular docking identified a previously uncharacterized allosteric pocket capable of accommodating the generative-AI-derived compounds. MM-PBSA analyses of MD trajectories provided binding free-energy estimates and revealed key residues that mediate ligand engagement and stabilize the inhibitor-protein complexes. Conclusion: S532, L556, H606, Y558, and F559 mediate ligand recognition, enabling elucidation of the mechanistic basis that underlies the interactions of two lead chemotypes. Several designed molecules exhibit marked isoform selectivity, displaying substantially stronger predicted affinity for DGKα than for DGKβ. Together, these insights provide a structural framework for the rational development of selective DGKα inhibitors. Citation Format: Farag E. Mosa, Khaled Barakat. Structure-guided discovery of potent and selective DGKα inhibitors for targeted cancer therapy abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5127.