This latest Chemunicate graphic (the Compound Interest side project that works with chemistry researchers and institutions to highlight their research in graphical form) was made for the Cambridge Crystallographic Data Centre, and takes a look at a particular computational method that can be used to assist in the discovery of drug molecules.

We don’t tend to give much thought to how drugs work. If you’ve got a headache, you’re probably not all that concerned with how taking an ibuprofen will help alleviate the pain – only that it will. Put simply, drugs work by binding to receptors, which are essentially ‘pockets’ on protein structures. Different drugs will bind to different receptors on different proteins, subsequently producing different effects.

When scientists are trying to develop drugs for a particular purpose, the discovery process will start with the identification of the target for the drug: the receptor that they want it to act on in order to produce an effect. Understanding the causes of diseases or conditions can help researchers know what they need to try and target with the drug. Once a target receptor is identified, they will then need to develop a drug molecule that fits this receptor, much like plugging a hole; the better the fit, the more effective the drug.

Though this might sound like it should be quite straightforward, in reality it’s anything but. Imagine you have hundreds keys, one of which fits a specific lock. Though it’s possible to find the correct key, it’s a time-consuming process. Now imagine the same scenario, but with with tens of thousands of keys instead of hundreds. This gives you some idea of the complexity of the task.

In trying to find a molecule that fits best with the receptor on a protein, researchers will often consider over ten thousand different compounds. Even then, they might not find a single molecule that’s a ‘perfect fit’ for the receptor they’re trying to target – to revisit the lock and key analogy, their huge collection of keys might not include a key that actually fits the lock in question. They might find a compound that’s a relatively good fit, in which case they can try to tweak its structure to improve the fit, but this can also prove challenging.

This is where protein fragment hotspots come in. Using the method detailed in the graphic accompanying this article, the regions in the receptor region of the protein that are most important for drug binding can be determined. These regions, referred to as ‘hotspots’, can then be mapped onto a computer model of the protein and give a good idea of where fragments of drug molecules will bind to the receptor.

Why is this useful? Well, if researchers know what interactions and areas are most important for binding at a receptor, they can tailor the molecules they’re trying to develop into drugs to take full advantage of these interactions. The method detailed in the graphic also allows an understanding of how molecule fragments can bind to these sites, as well as just their location, which can also help speed up the development of drug molecules.

To read more on this method, or to try out the CCDC’s fragment hotspot web app, check out their site here. You can also read the paper that this graphic is based on here.

Chemunicate creates commissioned graphics for chemistry researchers and institutions. If you’re interested in having a graphic made based on your research or some other topic, find out more here.

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