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New discovery in quest for better drugs


March 2014


These days, system behaviour can be simulated with an incredibly high level of precision thanks to software that is able to manage massive amounts of data and variables.

Computer simulations play an important role in scientific research and industrial planning, where highly complex machine and process design would not be possible without rigorous modelling.

In one very promising application of computer modelling in the medical and pharmaceutical fields, a team of scientists from D. E. Shaw Research and Columbia University in New York collaborated with Monash University in Australia to develop a greater understanding of how the body interacts with innovative drugs, paving the way for the future creation of more targeted drugs with fewer side effects.

The research employed computer modelling to examine the drug reception potential of G protein-coupled receptors (GPCRs), the key family of receptor proteins in humans that serve as binding sites for nearly half of all drugs on the world market today.

The team was able to map out alternative drug recognition sites on this protein that could be targeted by a new class of drug molecule. As these receptors are involved in nearly every kind of biological process and most diseases, the research has great implications for developing drugs that could combat neuropsychiatric disorders, cardiovascular disease, obesity and diabetes, inflammation and cancer.

Atomic-level computer simulations on a known crystal structure of GPCR yielded in-depth analysis on the receptor’s interaction with different drugs, revealing how these elements seek each other out and change shape and orientation during bonding.

These bonding mode predictions were put to the test in biological experiments, which the team used to validate their findings. The scientists were then able to apply the knowledge they gleaned from the process to make chemical modifications to the drug, creating a molecule that more effectively targets the GPCR.


Source: This study was published online by Nature on 13 October 2013