Researchers from the University of Cambridge, the University of Milan and Google Research have used machine learning techniques to predict how proteins, particularly those implicated in neurological diseases, completely change their shapes in a matter of microseconds.

They found that when amyloid beta, a key protein implicated in Alzheimer’s disease, adopts a highly disordered shape, it actually becomes less likely to stick together and form the toxic clusters which lead to the death of brain cells.

The results, reported in the journal Nature Computational Science, could aid in the future development of treatments for diseases involving disordered proteins, such as Alzheimer’s disease and Parkinson’s disease.

“We are used to thinking of proteins as molecules that fold into well-defined structures: finding out how this process happens has been a major research focus over the last 50 years,” said Professor Michele Vendruscolo from Cambridge’s Centre for Misfolding Diseases, who led the research. “However, about a third of the proteins in our body do not fold, and instead remain in disordered shapes, sort of like noodles in a soup.”

We do not know much about the behavior of these disordered proteins, since traditional methods tend to address the problem of determining static structures, not structures in motion. The approach developed by the researchers harnesses the power of Google’s computer network to generate large numbers of short trajectories. The most common motions show up multiple times in these ‘movies’, making it possible to define the frequencies by which disordered proteins jumps between different shapes.

“By counting these motions, we can predict which states the protein occupies and how quickly it transitions between them,” said first author Thomas Löhr from Cambridge’s Yusuf Hamied Department of Chemistry.

The researchers focused their attention on the amyloid beta peptide, a protein

What happens when municipalities in the Copenhagen area experience a COVID-19 flare-up? Would closing the schools have any effect, or would a better choice be directing the parents to work from home? Due to the COVID-19 pandemic, authorities worldwide have several times implemented steps to keep the pandemic in check.

Now, researchers from the Department of Computer Science at Aalborg University have come forward with a new agent-based model that can be used as a tool for making even better, informed choices regarding which restrictions to implement.

The background of using agent-based modeling to analyze, predict and control the rapid spreading of COVID-19 is described in the paper Fluid Model-Checking in UPPAAL for COVID-19 published in the distinguished conference proceedings series Lecture Notes in Computer Science.

167 fewer new cases a day

In the new model, the researchers simulate interactions between specific agents or in other words, individuals. Based on data from Statistics Denmark, the Danish Building and Housing Register (BBR), the Central Business Register and the State Serum Institute of Denmark, the researchers have used the agent-based technic to construct a model of Northern Jutland, with more than 500,000 individuals.

The region was placed under lockdown in November due to the fear of spreading the cluster-5 variant of the coronavirus, circulating primarily in farmed minks.

In the model, each individual is assigned a state of health, which is combined with general data on addresses, places of employment, family sizes and commuting patterns to calculate realistic simulations of the mobility patterns of all Northern Jutland inhabitants.

Project head, Professor Kim Guldstrand Larsen, explains that the researchers have simulated the case numbers in the region over