A first-of-its-kind model of a process important to the filaments that maintain our cells’ structure and integrity was published this month and relied heavily on the research of Raja Dey, a scientist in the Cellular and Molecular Biology research section of The Hormel Institute.

The article, “A proposed atomic model of the head-to-tail interaction in the filament structure of vimentin,” was published this month in the Journal of Biomolecular Structure and Dynamics and research was done at the University of Connecticut in collaboration with Dr. Peter Burkhard.

The structural integrity of cells is held together by the cytoskeleton, and the filaments Dey is interested in are long rope-like structures that are part of the cytoskeletal system.

“The intermediate filament proteins are present in both the cytoplasm and the nucleus, and they are involved in a large number of tissue-specific human diseases related to muscle, heart, skin and neuronal disorders,” Dey said.

To build these long filaments, vimentin (a small structural protein) goes through lateral assembly of 32 monomers to form unit length filament (ULF) followed by a head-to-tail interaction that links ULFs into the rope-like structures that maintain cellular integrity. The head-to-tail interaction is known to be critical to the formation of filaments, but no one had published a model of what that interaction actually looks like at the atomic level.

Dey’s research provides a computational model of what this head-to-tail interaction looks like which is further supported by a number of biophysical experiments. This model will allow scientists to better understand the interaction involved in longitudinal annealing process and to more easily study what happens when something in the process goes wrong — leading to mutations and the muscle, heart, skin and neuronal disorders these filaments are involved in.

Dey joined The Hormel Institute in November of 2018 and his expertise includes structural and computational biology along with biophysical characterization of macromolecules to predict their function. The work for this article was supported by NIH Grant 1P01GM096971.