In-silico study of small cell lung cancer based on protein structure and function: A new approach to mimic biological system

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.

Keywords

• Mouse double minute 2 and p53 complex protein
• small cell lung cancer
• umbrella sampling simulation