A research team had recently worked on a computational pattern that can forecast how key parameters might be “dialed up” or “dialed down.” Such a thing would conduct how molecules with more than one binding site interact with one another. Also, it could enhance the biological research and finding of new medicines.
The research is the first-of-its-kind based on molecular interactions. It was developed by a team of biomedical engineers from the University of Minnesota’s College of Science and Engineering. According to the side, their findings would make it easier and more accessible for other researchers to work on some new medicines or different types of known therapies for diseases such as autoimmune diseases, cancer, or HIV.
What Are the Team’s Discoveries and Conclusions
The research resulted in a mathematical structure that resembles the effects of the vital parameters that conduct interactions between molecules that have many binding sites, such as the case for many medicines. The team intends to utilize that computational pattern to design a web-based app. They also think that their work would help other researchers utilize it to enhance the development of some new therapies for diseases.
Casim Sarkar, from the University of Minnesota, is a biomedical engineering associate professor, also the senior author of the research. He released a statement about the research: “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.”
Moreover, the team analyzed three main parameters of molecular interactions-binding power of each part, the rigidity of the connection between the sites, and the dimension of the connection arrays. Wesley Errington, fro the University of Minnesota, detailed: “We think we’ve hit on rules that are fundamental to all molecules, such as proteins, DNA, and medicine, and can be scaled up for more complex interactions.”
