AI solves a challenge in minutes that would take neuroscientists weeks

A staggering 55 million people worldwide grapple with dementia-related disorders such as Alzheimer's and Parkinson's. These illnesses stem from the malfunction of some of the body's most fundamental building blocks, proteins. When these proteins clump together abnormally, they wreak havoc on cellular processes, leading to the progressive decline of cognitive function and other neurological impairments.

For decades, understanding the precise mechanisms behind protein clumping and its connection to neurodegenerative diseases has remained a daunting challenge. The complexity of these processes, coupled with the limitations of existing research tools, has hampered progress.

Researchers at the University of Copenhagen's Department of Chemistry have now developed a revolutionary AI tool that solves a challenge in minutes that would take neuroscientists weeks, marking a significant step forward in our fight against neurodegenerative disorders that affect the brain such as Alzheimer's and dementia.

A team led by Dr Jacob Kæstel-Hansen and Nikos Hatzakis from the Department of Chemistry have invented a groundbreaking machine learning algorithm that can track protein clumping in real-time using microscopic images. It automatically maps and analyzes the characteristics of these clumps, providing an unprecedented level of detail.

The new algorithm developed by the Copenhagen team shatters the limitations of traditional approaches. The tool analyzes data in minutes and can detect protein clumps as small as a billionth of a meter, revealing details invisible to the human eye. It can categorize clumps based on their size, shape, and other characteristics. Additionally, it can track the evolution of these clumps over time, offering insights into the dynamics of the clumping process.

Researchers can now investigate the specific factors that trigger clumping, identify potential targets for intervention, and explore how different protein conformations influence their effects on cellular function.

The researchers are optimistic that this novel algorithm can pave the way for the development of targeted therapies and drugs. By understanding the intricacies of protein clumping, scientists can design interventions that prevent or reverse the process, potentially halting the progression of neurodegenerative diseases.

What's next?

The researchers have already started using the novel AI tool to conduct experiments with insulin molecules. As insulin molecules clump, their ability to regulate blood sugar weakens, leading to conditions like diabetes. By using the tool to analyze insulin clumping, the team hopes to develop new drugs that maintain insulin efficacy and better manage blood sugar levels.

The researchers have made the algorithm freely available online as open-source software. This eliminates financial barriers and allows scientists worldwide to utilize this powerful tool. Collaborative efforts across borders can accelerate research and unlock new insights into protein clumping.

"As other researchers around the world begin to deploy the tool, it will help create a large library of molecule and protein structures related to various disorders and biology in general. This will allow us to better understand diseases and try to stop them," concludes Nikos Hatzakis from the Department of Chemistry.

The team’s findings have been published in the scientific journal Nature Communications, and the research has received support from the Novo Nordisk Foundation Center for Optimised Oligo Escape and Control of Disease.