Computational Biophysical Chemistry

Molecular Dynamics Simulations

Molecular dynamics (MD) simulations are computational techniques used to study the behavior of atoms and molecules over time. They model the physical movements and interactions of particles according to classical physics principles, such as Newton’s laws of motion. By simulating the interactions between particles based on classical mechanics, these simulations provide insights into the behavior of complex systems at the atomic and molecular levels.

MD simulations are a powerful tool across various fields. In science, they deepen our understanding of material properties and biological processes. In medicine and pharmaceuticals, they are crucial for elucidating disease mechanisms, designing and optimizing drugs, and advancing personalized medicine. By providing detailed atomic-level insights, MD simulations enable researchers to make informed decisions and accelerate discovery and development processes.

Exploring Mutant p53 Protein Dynamics Through Gaussian Accelerated Molecular Dynamics Simulations

Lung cancer remains the leading cause of cancer-related deaths among men globally, while breast cancer holds the same position among women. Understanding the molecular biology of cancer requires identifying and analyzing the mutations that drive these diseases.

Since somatic mutations occur more frequently than germline mutations, they have become critical focal points for research. Among the top three genes commonly mutated in both breast and lung cancers is TP53. TP53 encodes the tumor protein p53, a 393-amino acid protein crucial for preventing cancer development. Mutant forms of p53, in addition to their prominence in breast and lung cancers, are found in over 50% of all human cancers.

This research project focuses on exploring the dynamic behavior of both wild-type and mutant forms of p53 protein (namely, E285K, G245C, R158L, R175H, R175L, R248L, R248Q, R248W, R249S, R273C, R273H, R273L, V157F, Y163C, and Y220C) through molecular dynamics simulations. Specifically, it leverages Gaussian Accelerated Molecular Dynamics to investigate the distinct patterns of these p53 mutants, offering new insights into their dynamical properties.

Scroll to Top