RAHMAN

• Name: RAHMAN
• EuroHPC machine used: MareNostrum 5
• Topic: Natural sciences (Computer and information sciences, Biological sciences, Chemical sciences)

Overview of the project

Integrity of our genomic material is critical for cell survival and faithful inheritance. The newly identified Rahman syndrome (RS) is a rare genetic disease linked to frameshift mutations in the linker-histone H1.4, a critical structural constituent of chromatin, i.e., our genomic material. All disease-causing mutations are located within a narrow locus encoding the carboxy-terminal domain (CTD) of linker histone H1.4, the protein responsible for the compaction of our genomic DNA. The preliminary findings of the project indicate that the altered electrochemical composition of RS H1.4 CTD disrupts the proper genome packaging in the specific case of the one patient, however to what extend the same mechanism applies to all other known patients is not clear. Furthermore, RS is known to impair DNA repair, suggesting epigenetic alterations reminiscent of a complex disease physiology. The project aimed to tap into EuroHPC resources to expand their atomistic-level high performance simulations to characterize the dynamics of all unique RS H1.4 mutants in comparison to their wild-type (WT) counterparts. The goal of this full-scale investigation was to map out the “molecular phenotype” landscape of the RS to improve understanding of this little-known disease and suggest a potential roadmap toward personalized clinical solutions.

How did EPICURE support the project and what were the benefits of the support?

“In our prior supercomputing environments, our molecular dynamics engine Gromacs was not running efficiently across more 4 nodes. Thanks to EPICURE support, both our classical molecular dynamics and enhanced sampling methods were optimized, enabling them to scale effectively across up to 10 nodes. The EPICURE team introduced us parallel scaling options available in Gromacs we did not know to use effectively before.

EPICURE provided specific optimizations and performance improvements tailored to our large and complex molecular systems. Thus, we significantly reduced the computation time and improved the efficiency of our simulations, which greatly accelerated our project.” – Seyit Kale, Research Group Leader