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The study resulted in a mathematical framework that simulates the effects of the key parameters that control interactions between molecules that have multiple binding sites, as is the case for many medicines. Researchers plan to use this computational model to develop a web-based app that other researchers can use to speed the development of new therapies for diseases.
The research is published in the Proceedings of the National Academy of Sciences (PNAS), one of the world's most-cited and comprehensive multidisciplinary scientific journals.
“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.”
The research team studied three main parameters of molecular interactions—binding strength of each site, rigidity of the linkages between the sites, and the size of the linkage arrays. They looked at how these three parameters can be “dialed up” or “dialed down” to control how molecule chains with two or three binding sites interact with one another. The team then confirmed their model predictions in lab experiments.
“At a fundamental level, many diseases can be traced to a molecule not binding correctly,” said Wesley Errington, a University of Minnesota biomedical engineering postdoctoral researcher and lead author of the study. “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 to decode this programming language 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. By dialing the parameters in this new mathematical model, researchers can quickly understand how these different binding configurations are affected, and tune them for a wide range of biological and medical applications.
“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 “It’s really a molecular signature that we can use to study and to engineer molecular systems. The sky is the limit with this approach.”
In addition to Sarkar and Errington, the research team included Bence Bruncsics from the Budapest University of Technology and Economics who was a visiting masters’ student in the Sarkar lab at the University of Minnesota. The team also partnered with the Institute for Therapeutics Discovery & Development (ITDD) in the University of Minnesota’s College of Pharmacy for the lab experiments to test the computational model. The research was funded by the National Institutes of Health.
To read the full research paper entitled “Mechanisms of noncanonical binding dynamics in multivalent protein–protein interactions” visit the PNAS website.
