In recent decades, high-performance computing (HPC) has become an indispensable tool for biomedical science. In cancer research, for example, computational methods using supercomputers can quickly focus researchers’ attention on details at the molecular level that hold the keys to understanding and potentially treating disease. Such methods have been particularly important in the field of personalized medicine, which aims to develop more effective drugs that target specific molecular features of individual patients’ tumors.
Feb 03, 2025
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“With the refinement of computational tools that has been going on in the last 40 years, computations have become an important asset for biophysics and pharmaceutical research,” says Dr. Giovanni Settanni, who conducts research in computational biophysics at Ruhr University Bochum and Johannes Gutenberg University of Mainz (JGU). “Simulations can be seen as a sort of in silico microscope with molecular level resolution, and they help interpret experimental data which can hardly reach the same level of resolution.”
Settanni is an expert in the field of molecular dynamics (MD) simulation, an approach that uses principles from biophysics to develop atomic-level models of molecules. One way this method is being used in biomedical research is to predict changes in protein structure, alterations that affect a protein’s ability to interact with and bind to other molecules. Because MD simulations offer extremely high resolution and reveal how protein shape can change over time, they can only be done using supercomputers. Recent work in the Settanni Lab using the Hawk supercomputer at the High-Performance Computing Center Stuttgart (HLRS) demonstrates how this approach can help identify new approaches to treating cancer.
Dr. Settanni has been collaborating with the group of structural biologist Dr. Andreas Joerger of the Goethe University Frankfurt to study an important protein called p53. In a healthy human body, p53 serves as a bulwark against cancer by regulating how cells divide and repair damage to DNA. When a mutation occurs in p53, however, an alteration in its genetic code can produce a change in amino acid sequence that also changes the protein’s 3D structure. This can cause it to lose its normal regulatory abilities, leaving cancer cells free to divide and spread uncontrollably. In fact, roughly half of all cancer cases are linked to mutations that inactivate the p53 protein.
One approach in cancer research involves searching for drugs that can neutralize harmful mutations by binding to proteins. In the case of p53, this means looking for small molecules that can stabilize the mutants and return the protein to its original shape so that it can function normally. Until recently, however, researchers have considered p53 “undruggable,” as its surface appeared to be virtually impervious to binding with small molecule drugs.
In prior research, Joerger showed that it is possible to target a specific mutation in p53 called Y220C. The Y220C mutation causes a specific physical change in p53, creating a destabilizing surface crevice that results in a loss of the protein’s normal shape at body temperature. His lab showed that it is possible to find small molecules that bind to the surface of the Y220C cavity, restoring p53 to its proper shape and its ability to perform its anti-cancer function. That previous result led to a clinical trial to test the safety and effectiveness of this approach in humans.
With this encouraging success, Joerger is now looking for other potentially druggable mutations in p53. He would also like to understand more generally whether or not mutation-specific small molecules offer an effective way to restore the critical protein’s function. It is here that high-performance computing is now making an important contribution.
In earlier work, Joerger primarily used a laboratory approach called X-ray crystallography to understand protein structure. This approach works like a high-resolution microscope, beaming a crystallized protein with X-rays to create an atomic level snapshot of it. More recently, however, he has worked with Settanni to incorporate molecular dynamics simulation into his research. MD simulations complement X-ray crystallography by delivering a more comprehensive, high-resolution understanding of how protein shape can change over time. The approach can deliver new insights into a protein’s behavior and how its susceptibility to drugging might alter under different conditions.
Three images of the same location on the p53 protein. The first image shows the location without a mutation. The center image shows the crystal structure of the Y163C mutant using X-ray crystallography. A representative snapshot using molecular dynamics simulation (right) reveals that the cavity can become significantly wider, offering a potential binding site for small molecules. Image: Balourdas et al. 2024.
As reported in a recent paper in the Nature journal Cell Death & Disease, the team investigated a specific category of structurally unstable cancer-causing mutations in p53. As part of this effort, Settanni used HLRS’s Hawk supercomputer to run multiple simulations of several mutants of p53. The simulations, also including the surrounding solvent, contain between approximately 36,000 and 39,000 atoms, and make it possible to follow the time evolution of each mutant cumulatively for 0.8 microseconds.
“Simulations helped characterize the behavior of the less stable mutants of p53, which are difficult to handle experimentally,” Settanni said. “Simulations are able to identify the presence of cavities that can form on the surface of the mutant protein. These cavities can be targeted by drugs to stabilize the protein.”
According to the paper, a mutation called Y163C turned out to be especially interesting. Although the static image generated using X-ray crystallography seemed to indicate that Y163C would not be a good target because the cavity created by the mutation is very small, MD simulations revealed that the shape of this pocket can become drastically enlarged, providing ample room for a small molecule to bind with the protein. This finding has helped the researchers to focus their energies on Y163C in favor of other mutations that MD and other approaches showed would be more resistant to binding.
Molecular dynamics simulation also provided structural details of the Y163C cavity that could help to identify specific small molecules capable of binding to p53. This information can support virtual drug screening, another approach in computational biology in which catalogs of data describing many thousands of different kinds of small molecules are compared to molecular and structural features of a potential binding site. Such a virtual screen can quickly identify which small molecules have chemical structures that are most likely to be compatible, removing the prohibitively labor-intensive need to test every potential interaction in the laboratory. For those relatively small number of small molecules that show high likelihood of interacting with p53, laboratory experiments can then quickly investigate that much more limited number of computational predictions. And if these experiments are ultimately successful, the findings could then be used to justify clinical trials exploring whether using a small molecule to target Y163C is safe and effective in humans.
There is no guarantee that Y163C will turn out to be an effective target for stopping cancer. The collaboration between Joerger and Settanni demonstrates, however, how molecular dynamics simulation using high-performance computing can make unique contributions to cancer research, accelerating the advance of personalized cancer medicine.
— Christopher Williams
Balourdas DI, Markl AM, Krämer A, et al. 2024. Structural basis of p53 inactivation by cavity-creating cancer mutations and its implications for the development of mutant p53 reactivators. Cell Death Dis. 15:408.
Funding for Hawk was provided by Baden-Württemberg Ministry for Science, Research, and the Arts and the German Federal Ministry of Education and Research through the Gauss Centre for Supercomputing (GCS).
