Inhibition of LONP1 in prostate cancer: bibliometrics-guided target screening and AI-driven antibody design

Background: Prostate cancer remains one of the most common malignant tumors among men worldwide, with its incidence showing a continuous global increase. In recent years, mitochondria-targeted therapeutic strategies have emerged as a prominent research focus in oncology. However, a systematic analysis of the research trends concerning mitochondria in prostate cancer treatment is currently lacking. This study employed bibliometric methods to conduct a comprehensive analysis of the dynamic progress in mitochondria-related prostate cancer research, ascertain its significant role, and identify potential mitochondria-targeted therapeutic targets. Furthermore, using computer-aided methods, we designed and optimized a specific antibody, providing a candidate strategy for prostate cancer control. Methods: This study utilized the Web of Science Core Collection database (2015–2023) to perform visual analysis of country-keyword network relationships using CiteSpace and the Bibliometric Online Analysis Platform. Target screening was conducted by integrating bioinformatics and research intelligent agents. Subsequently, inhibitory antibodies were designed and screened based on GeoBiologics, followed by systematic in vitro evaluation of their purity, antigen-antibody affinity, conformational stability, colloidal stability, and enzymatic inhibitory activity. Results: The role of mitochondria in prostate cancer has garnered significant attention. Research trends have shifted from fundamental mechanisms to addressing drug resistance, developing novel delivery systems, and exploring combination therapies, highlighting mitochondria as a promising target for clinical intervention. Lon Peptidase 1(LONP1) is closely associated with mitochondrial homeostasis and prostate cancer progression. Antibody_82-M1 effectively blocks the ATP-binding site of LONP1, demonstrating high affinity, favorable stability, a high degree of humanization, and excellent drug-like properties, indicating strong potential for clinical translation. Conclusion: The designed LONP1 inhibitory antibody offers a novel strategy for prostate cancer treatment. The proposed workflow – “bibliometric guidance – research intelligent agent screening – bioinformatics support” – proves beneficial for enhancing AI-driven scientific research and optimizing the screening of therapeutic targets for diseases.