New method to develop new medicines
New York: A new mathematical framework on molecular interactions will make it easier and more efficient for scientists to develop new medicines and other therapies for diseases such as cancer, HIV and autoimmune diseases, say reseachers.
The mathematical framework simulates the effects of the key parameters that control interactions between molecules that have multiple binding sites.
As is the case for many medicines, the researchers said in a study, published in the journal Proceedings of the National Academy of Sciences (PNAS).
The researchers have planned 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 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 Indian-origin researcher and study senior author Casim Sarkar from University of Minnesota in the US.
“This computational model will make research much more efficient and could accelerate the creation of new therapies for many kinds of diseases,” Sarkar added.
For the findings, 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 ‘dialled up’ or ‘dialled 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 study lead author Wesley Errington.
“By understanding how we can manipulate these ‘dials’ that control molecular behaviour, we have developed a new programming language that can be used to predict how molecules will bind,” Errington added.
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 dialling 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,” Errington added.