Huntington’s disease (HD) is a devastating neurological disorder that causes death in neurons – a major component of the brain – and eventually the loss of physical and mental abilities. Currently, there are only very limited treatment options. IBM and CHDI Foundation have published research in the Journal of American Chemical Society1 detailing structural features of huntingtin proteins that could point toward future therapeutic strategies that should consider both the way the protein aggregates (accumulates and clumps together) and the way it interacts with other proteins.
HD is caused by a genetic mutation in the polyglutamine (polyQ) region of the huntingtin protein, and a diagnosis is made by determining the number of glutamine amino acid repeats in an individual’s polyQ region.
In non-HD individuals, the huntingtin protein has a polyQ length of 22 or fewer glutamines. If the protein measures 39 glutamines or more, an individual will develop HD at some stage in their life; typically, the longer the Q-length, the earlier symptoms present themselves.
In an effort to understand how the length of these polyQ sequences affects the structure and function of mutated huntingtin proteins, a joint team from IBM and CHDI used large-scale computer simulations to study the structural features of the exon 1 portion of the protein (a small fragment of the protein implicated in HD pathology) with five different Q-lengths, ranging from 22 to 561.
Figure 1: Examples of polyQ structures for five different Q-lengths.
Two major findings are reported in this research:
An indication that lengthening of the polyQ region leads to increases in the beta-sheet (a type of protein structural motif) content of the structure. The linkage between longer polyQ regions and increased beta-sheet structures could potentially lead to new therapeutic approaches targeting the beta-sheets. These approaches may aim to prevent or reverse beta-sheet formation for these mutant proteins, thus possibly delaying or preventing the huntingtin protein’s aggregation.
More importantly, investigators observed “glue-like” behavior within glutamine side chains, a phenomenon that resulted in super-compacted polyQ structures in huntingtin proteins that have disease associated Q lengths. This glue-like behavior might interfere with these disease-associated huntingtin proteins’ ability to bind to other proteins; an appropriate protein-protein interaction network is required to maintain normal brain function. This finding could potentially open another route for future drug design targeting the protein’s interactions.
Revealing these structural characteristics, which are in good agreement with recently-published experimental observations, sheds light on how the mutation may contribute to the protein’s toxicity, functionality or degradation, and the eventual progression of the disease.
A better understanding of the structural features of these intrinsically disordered proteins and their interactions with other proteins might help in future therapeutics for Huntington’s. For example, a detailed molecular picture of the changes in mutated huntingtin protein structures might facilitate a novel drug design targeting the protein’s aggregation and key interaction sites with other proteins, opening new opportunities for therapeutic research.
“It’s very exciting to be collaborating with IBM on this project with the remarkable computational power that they can bring,” says Dr. Leticia Toledo-Sherman, Director of Computer Aided Drug Design & Medicinal Chemistry at CHDI Management. “This computational capacity allied with their expertise is poised to give us new insights into the structural landscape of the disease-causing huntingtin protein.”
This research is part of ongoing work between IBM Research and CHDI that applies big data analytics toward bettering our understanding of Huntington’s disease. In particular, the two organizations aim to build disease progression models and enhance the efficacy of medications, with the objective of improving the lives of people afflicted by the condition.
Read a special personal viewpoint by the two lead authors Aditya Kashyap and Anna Fomitcheva Khartchenko here. Immunofluorescence (IF) is a milestone method which was introduced by Albert Coons in 1942. It uses fluorescent antibodies for tagging specific antigens in biological tissues. With this simple molecular complex, cell biology has been transformed into a visual science […]
Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists.