New model better simulates chemical interactions, may improve drug making

Researchers have developed a new model which simulates the effects of key factors controlling the interactions between complex molecules, an advance that may lead to the development of novel therapies for diseases like cancer, HIV, and autoimmune diseases.

The study, published in the journal PNAS, looked at three main parameters playing a role in the interactions between molecules -- binding strength of each of the chemical's site, rigidity of the linkages between sites, and the size of the linkage arrays.

As part of the study, the researchers, including those from the University of Minnesota in the US, assessed how these three parameters can be "dialed up", or "dialed down" to control how molecule chains with two or three binding sites interact with each other.

"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 study co-author Casim Sarkar from the University of Minnesota.

According to the researchers, many diseases can be traced to a molecule not binding correctly.

Understanding how to manipulate these 'dials' controlling molecular behaviour, they said, may lead to the development of a new programming language which can be used to predict how molecules will bind.

"This computational model will make research much more efficient and could accelerate the creation of new therapies for many kinds of diseases," Sarkar said.

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 researchers said.

Hence, they believe there is a need for a mathematical framework to decode this programming language.

By feeding input parameters in the new mathematical model, scientists can quickly understand how these different binding configurations are affected, and tune them for a wide range of biological and medical applications, the study noted.

"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. 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," said Wesley Errington, another co-author of the study from the University of Minnesota.