Computational model of molecular interactions could improve new medicine development

A study by researchers at the University of Minnesota’s College of Science and Engineering, US has produced a mathematical framework that can simulate how different parameters control interactions between molecules that have multiple binding sites. According to the team, their findings could aid in the development of new therapies.

The researchers established how binding strength of each site, rigidity of site linkages and the size of linkage arrays control how molecules with multiple binding sites interact with one another by altering them and observing the effects. The team then confirmed their model predictions in lab experiments.

“The big advance with this study is that usually researchers use a trial-and-error experimental method in the lab for studying these kinds of molecular interactions, but here we developed a mathematical model where we know the parameters so we can make accurate predictions using a computer,” said Casim Sarkar, a University of Minnesota biomedical engineering associate professor and senior author of the study. “This computational model will make research much more efficient and could accelerate the creation of new therapies for many kinds of diseases.”

Wesley Errington, a University of Minnesota biomedical engineering postdoctoral researcher and lead author of the study explained: “At a fundamental level, many diseases can be traced to a molecule not binding correctly. By understanding how we can manipulate these ‘dials’ that control molecular behavior, we have developed a new programming language that can be used to predict how molecules will bind.”

The need for a mathematical framework is highlighted by the researchers’ finding that, even when the interacting molecule chains have just three binding sites each, there are a total of 78 unique binding configurations, most of which cannot be experimentally observed. The new computational model allows for a quick understanding of how different parameters affect binding.

“We think we’ve hit on rules that are fundamental to all molecules, such as proteins, DNA and medicines and can be scaled up for more complex interactions,” said Errington.

The researchers intend to create a web-based app from their computational model enabling other researchers to speed the development of new therapies for diseases.

The study was published in PNAS.