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Published on: October 10, 2019
Inhibition of LONP1 in prostate cancer: bibliometrics-guided target screening and AI-driven antibody design
Jiawei Pan1, Yuan Zhang, Anqi Yang
1Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
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.
Insights
This study identifies mitochondria as a key target for prostate cancer treatment, developing a novel antibody against LONP1. This approach offers a promising new strategy for controlling prostate cancer progression.
Area of Science:
- Oncology
- Mitochondrial Biology
- Drug Discovery
Background:
- Prostate cancer incidence is rising globally.
- Mitochondria-targeted therapies are a growing focus in cancer research.
- A systematic analysis of mitochondria in prostate cancer treatment research was lacking.
Purpose of the Study:
- To analyze research trends in mitochondria-related prostate cancer studies.
- To identify potential mitochondria-targeted therapeutic strategies.
- To design and optimize a novel antibody for prostate cancer control.
Main Methods:
- Bibliometric analysis of Web of Science data (2015-2023) using CiteSpace.
- Integration of bioinformatics and AI agents for target screening.
- Design and in vitro evaluation of inhibitory antibodies against identified targets.
Main Results:
- Research trends focus on drug resistance, delivery systems, and combination therapies.
- Lon Peptidase 1 (LONP1) is identified as crucial for mitochondrial homeostasis and prostate cancer.
- Antibody_82-M1 shows high affinity, stability, and potential for clinical translation by blocking LONP1's ATP-binding site.
Conclusions:
- A LONP1 inhibitory antibody presents a novel prostate cancer treatment strategy.
- The workflow enhances AI-driven research and therapeutic target screening.
- This study provides a candidate strategy for prostate cancer control.
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