Protein clumping is the underlying cause of many brain-related disorders like Alzheimer’s and dementia. A new tool has been created by researchers to detect and study these small protein clumps. This breakthrough could lead to a better understanding of the body’s basic building blocks and improved treatments for diseases like cancer, Alzheimer’s, and Parkinson’s. Nearly 100,000 Danes over 65 and over 55 million people worldwide suffer from dementia-related disorders such as Alzheimer’s and Parkinson’s. These illnesses occur when certain proteins form small clusters.The clumping together of essential building blocks in the body leads to the destruction of crucial functions, but the reasons behind this and how to address it are still not fully understood by scientists. The difficulty in studying this phenomenon has been due to a lack of appropriate tools. Now, a team of researchers from the Hatzakis lab at the University of Copenhagen’s Department of Chemistry has developed a machine learning algorithm that is capable of monitoring clumping under the microscope in real-time. This algorithm can automatically identify and track the key characteristics of the clustered building blocks that contribute to Alzheimer’s and other neurodegenerative disorders.rs. Up until this point, it has been an impossible task.
“Our algorithm can now solve a challenge that typically takes researchers several weeks in just a matter of minutes. This breakthrough will hopefully make it easier to analyze microscopic images of clumping proteins, ultimately enhancing our understanding and potentially leading to new treatments for neurodegenerative brain disorders in the long run,” explained PhD Jacob Kæstel-Hansen from the Department of Chemistry, who co-led the research team with Nikos Hatzakis.
Quick Detection of Microscopic Proteins
The interaction and communication of compounds and signals among proteins and other molecules can now be detected rapidly.Proteins play a crucial role in the functioning of our cells, occurring frequently in natural processes. However, when mistakes occur, proteins can clump together in a way that hinders their normal functioning. This can result in the development of neurodegenerative disorders in the brain and even cancer.
The researchers have developed a machine learning algorithm that is capable of identifying protein clumps in microscopy images down to a billionth of a meter. Additionally, the algorithm can categorize and group these clumps based on their shapes and sizes, while also tracking their progression over time. The appearance of these clumps can significantly affect their functionality.The behavior of clumps in the body, whether positive or negative, can be observed through a microscope. Some clumps appear round, while others have filamentous structures, and their shape can vary depending on the disorder they cause. However, manually counting the clumps thousands of times is a time-consuming process, as noted by Steen Bender from the Department of Chemistry, the article’s first author. The algorithm will make it easier to study why clumps form, leading to the development of new drugs in the future.Researchers are currently exploring various therapies to combat these disorders, and the fundamental understanding of these clumps depends on the ability to see, track, and quantify them over time. According to one expert, no other methods can currently do so automatically and as effectively.
The Department of Chemistry researchers are using freely available tools to conduct experiments with insulin molecules. As insulin molecules clump, their ability to regulate blood sugar weakens. The new tool allows researchers to observe and study this undesirable clumping in insulin molecules.The model can assist in understanding how to prevent or change the clumps into safer or more stable forms by studying the impact of different compounds on the clumps, according to Jacob Kæstel-Hansen.
Once the microscopic building blocks are identified, the researchers believe that the tool has the potential to aid in the development of new drugs. Their work aims to stimulate the accumulation of more extensive information about the structures and behaviors of proteins and molecules.
By using the tool, other researchers worldwide can contribute to the creation of new drugs.The Department of Chemistry has assembled a vast collection of molecule and protein structures related to various disorders and biology. Nikos Hatzakis believes that this resource will enhance our understanding of diseases and aid in efforts to prevent them. The algorithm is available as open source on the internet, making it accessible to scientific researchers and others interested in studying the aggregation of proteins and other molecules.
