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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
In-silico study of small cell lung cancer based on protein structure and function: A new approach to mimic biological
Nidhi Sood1, Sameer Chaudhary1, Tanvee Pardeshi1
1Department of Computational Chemistry, RASA Life Science Informatics, Pune, Maharashtra, India.
Abstract:
Lung cancer being the most common disease worldwide that leads to a number of deaths. A huge amount of effort has been done in screening trials for early diagnose treatment which increases the disease-free survival rate. Based on the expression of protein of mouse double minute 2 and tumor protein 53 complex, we have identified the antagonist for this complex that would facilitate the treatment for specific lung cancer. It is a complex disease that involves vast investigation for the characterization of a lung cancer and thus, computational study is being developed to mimic the in vivo system. In this work, a computational process was employed for the identification of these proteins, with a short and simple method to discover protein-protein interactions. Moreover, these proteins have more similarities in their function with the known cancer proteins as compared to those identified from the protein expression specific profiles. A new method that utilizes experimental information to improve the extent of numerical calculations based on free energy profiles from molecular dynamics simulation. The experimental information guides the simulation along relevant pathways and decreases overall computational time. This method introduces umbrella sampling simulations. A new technique umbrella sampling is described where the high efficacy100 of this technique enables uniform sampling with several degrees of freedom. Here, we review the protein interactions techniques and we focus on main concepts in the molecular of in-silico study in lung cancer. This study recruiting new methods proved the efficiency and showed good results.
Insights
Researchers identified a novel antagonist for the mouse double minute 2 and tumor protein 53 complex, offering a new treatment avenue for specific lung cancers. This computational study enhances early diagnosis and treatment strategies for lung cancer.
Area of Science:
- Oncology
- Computational Biology
- Biochemistry
Background:
- Lung cancer is a leading global cause of mortality, necessitating advancements in early detection and treatment to improve survival rates.
- The mouse double minute 2 (MDM2) and tumor protein 53 (TP53) complex plays a critical role in various cancers, making it a target for therapeutic intervention.
- Computational approaches are increasingly vital for characterizing complex diseases like lung cancer and simulating biological systems.
Purpose of the Study:
- To identify an antagonist for the MDM2-TP53 complex for targeted lung cancer treatment.
- To develop and apply efficient computational methods for discovering protein-protein interactions.
- To leverage experimental data to enhance molecular dynamics simulations for biological pathway analysis.
Main Methods:
- Utilized a computational process for identifying key proteins and discovering protein-protein interactions.
- Employed molecular dynamics simulations, incorporating experimental information to guide calculations.
- Introduced and applied umbrella sampling simulations for enhanced sampling of molecular interactions.
Main Results:
- Identified proteins with functional similarities to known cancer-related proteins.
- Demonstrated that the new computational method, integrating experimental data, improves simulation efficiency.
- The umbrella sampling technique proved highly effective for uniform sampling across multiple degrees of freedom.
Conclusions:
- The study successfully identified potential therapeutic targets by analyzing the MDM2-TP53 complex.
- New computational methods, including umbrella sampling, significantly enhance the efficiency and accuracy of in-silico studies in lung cancer research.
- These advancements offer promising strategies for the early diagnosis and treatment of lung cancer.
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