Irn-Bru and Hairball Diagrams

What does Irn-Bru taste like?

I recently moved to Scotland, so this is a question I’ve been asking people. Sometimes they tell me it tastes like bubble gum, then someone else will argue that “Nae, it’s more like vitamins.” After a few pints, they generally all agree that “Irn-Bru tastes like Irn-Bru.” Of course, this helps not at all. But it’s got me thinking about a bigger, more science-y question: How do we describe and understand the unfamiliar?

It turns out that we do this by looking for similarities or differences to things we already know. Irn-Bru is like bubble gum or vitamins. But then again, it’s not. So  what happens when we don’t have a frame of reference? The question is not purely academic. In fact, it has important consequences for the treatment of serious infections.

A major challenge for scientists developing new drugs is finding effective targets while at the same time protecting patients from harmful side effects. Ideally, anti-fungal drugs will be toxic for the germ, but safe for the patient. The trick is to find things that are different between the fungus and normal, healthy human cells. Scientists are helped by the wealth of basic research that has been done using models: research in fruit flies and baker’s yeast have told us a lot about the types of proteins that are out there and how they accomplish the same tasks in flies and yeast that our own cells must perform. We can use similarities between simple organisms and our own cells to understand what happens during infections without having to start from scratch. A number of drugs that are very effective at treating fungal infections have been developed using this model.

But what happens when we encounter the equivalent of Irn-Bru- some totally novel protein – in a cell? Rather than looking for similarities, we have to start from the beginning – by looking at its physical properties. We can determine its composition, how it folds, and how its shape is affected by pH or temperature. Using this approach, how would we describe Irn-Bru? Well, it’s an orange colored drink, carbonated, and made according to a secret recipe with 32 ingredients. It’s usually served chilled. It has a label that says “Irn-Bru.” But while all this tells you how to find it in a shop, it doesn’t tell you what it tastes like. In fact, we do have one more piece of information: “Irn-Bru tastes like Irn-Bru.” We know that things that taste familiar are not Irn-Bru. This means that even if you’ve never tried it before, you could probably pick it out in a blind taste test because it is unfamiliar.

This is exactly what we do in the field of medical microbiology when looking for drugs that will cure serious fungal infections without harming patients. The goal is to find differences between harmful yeast and humans. In a paper I published this summer, I used this idea of like and unlike to generate what’s known as a hairball diagram of all the similar and dis-similar proteins of an organism called Cryptococcus neoformans (I’ve got a post about crypto here, if you’re the curious type). Just by taking any genes that Crypto has that are the most similar to each other, I found that a number of the genes that are the most similar are also totally unique to fungi.

Medical mycologists have already had some success using this principle to find new drug targets. A new class of drugs called the echinocandins has given doctors an important tool for treating fungal infections. Echinocandins bind to a protein that yeast use to build their cell walls, but that human cells don’t use at all. As a result, echinocandins are both safe and effective. However, the problem of drug resistance, which can occur when target proteins change shape or when the yeast finds some other way around the drug, means that we still need other drug targets. Fortunately, nearly half of the proteins that make up the arsenal of fungal pathogens like Candida albicans are totally unique. These are ideal drug targets. But, like Irn-Bru, we don’t have any way of understanding what they’re like.

A number of efforts are currently underway to solve this problem. Because proteins are encoded in genes, scientists are busy sequencing the genomes of as many different organisms as possible. The last 15 years have seen an explosion in this field. There have been large-scale efforts such as a project to collect and sequence marine microbes from around the world. Increasingly, undergraduates have gotten involved with student-driven projects focused on sequencing single organisms. At the same time, computational biologists are working to develop algorithms that can make sense of all these data. Scientists at the University of Washington launched the Foldit project to attract the online gaming community to tackle computer-assisted folding predictions. Famously, the gamers were able to solve a problem in 10 days that had stumped scientists for 15 years. The result is an ever-growing database of proteins that can be searched for patterns and grouped according to “like” or “unlike.”

These kinds of physical descriptions are only a start. In order to figure out which of these new proteins will be good drug targets for treating fungal infections, scientists will still have to test each one, searching for proteins that are important for growth and then finding drugs that bind those proteins. But by starting with targets that we know are different between healthy human cells and those that we’re trying to target, we can speed up the process of finding new, safe and effective drugs.

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