In Silico Modeling CD70 Receptor & Binding Sites for CAR T Therapy in T-Cell Lymphoma
AJAS · 2026 Computational Biology and Bioinformatics (inferred)
Overview
Lymphoma arises from the uncontrolled proliferation of lymphocytes, often triggered by genetic mutations. Standard treatments such as chemotherapy and radiation therapy face limitations due to toxicity, resistance, and relapse. This underscores the importance of precision approaches, such as CAR T-cell therapy, which modifies patient-derived T cells to target malignant receptors selectively. The aim of this research is to model the 3D structure and binding site of the CD70 receptor using computational tools to support the development of targeted CAR T-cell therapy for lymphoma. I hypothesize that the structure and binding sites of the CD70 receptor can be accurately modeled using AI-based computational tools. Then effective nanobody candidates can be identified to enhance CAR T-cell therapy in lymphoma patients. The CD70 receptor sequence data were retrieved from UniProt, followed by structural prediction using AlphaFold 3 and topology visualization with PROTTER. Binding site identification employed P2Rank and ScanNet, while docking simulations were carried out using HDOCK. Finally, PLIP was applied to quantify non-covalent interactions such as hydrogen bonds, salt bridges, and hydrophobic contacts. The modeling results revealed key structural features of CD70, including its trimeric configuration and electrostatic charge distribution. Binding site predictions from P2Rank and ScanNet were consistent, providing confidence in the docking strategy. Among nanobody candidates tested, 5m7q emerged as the most promising binder, forming the most significant number of stabilizing interactions with CD70. These interactions included hydrogen bonding, hydrophobic stabilization, and aromatic stacking, collectively suggesting strong specificity and binding affinity. The study highlights CD70 as a viable CAR T-cell target, with nanobody 5m7q offering potential as a CAR construct candidate. However, the work remains limited to in silico simulations. Experimental validation is essential to confirm computational predictions, including in vitro assays for binding affinity and functional immune activation, followed by in vivo studies to assess therapeutic efficacy. In future research, I will be focusing on optimizing CAR design for clinical translation. This work demonstrates the power of machine learning-driven structural modeling and molecular docking in identifying therapeutic candidates. By targeting CD70, the study lays the groundwork for developing CAR T-cell therapies with enhanced specificity and reduced toxicity compared to conventional treatments. The current research will help in developing CD70-targeted CAR T-cell therapies using a nanobody to achieve more specific and less toxic treatments for T-cell lymphoma.
Competition history
- AJAS 2026
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Source: AAAS Annual Meeting (Confex) / American Junior Academy of Science