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Duration:07:43
Uploaded:2019-05-27
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Coming up with brand new drugs is all about pinpointing and exploiting a disease’s weakness. A big part of perfecting drug design will be learning to predict how proteins get their shapes because that has everything to do with how both diseases and drugs work.


Hosted by: Hank Green

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Image Sources:
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https://commons.wikimedia.org/wiki/File:Bruker_Avance_DPX_250_NMR_Spectrometer.jpg

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Thanks to CuriosityStream for supporting this episode of SciShow.  Go to curiositystream.com/scishow to learn more. (Intro) Coming up with brand new drugs is all about pinpointing and exploiting a disease's weaknesses, and we'd like to think that medical researchers sit down and try to figure out what would be the most efficient way to do that from the very beginning, but while we want it to work that way, it doesn't always. How we find and target a disease's weaknesses has changed over time and we're still working on it.  A big part of perfecting drug design will be learning to predict how proteins get their shapes, because that has everything to do with how both diseases and drugs work.  See, genes and our DNA code for proteins.  A gene is a linear string of four letter code and our cells translate that into a string of amino acids.  To become a mature protein, that string twists and folds into an incredibly specific shape that dictates how it's going to function.  It's a little more complicated than folding laundry.   There are around 20 different amino acids and their interactions with each other and the cellular environment lead to a dizzying array of different shapes.  So when mutations crop up in our genes, that affects both the sequence of the gene and the perfectly folded product, as well as its function.  This is often the starting point for diseases. Likewise, the starting point for a drug will often be to bring that function back or stop a diseased wrong function from happening.  We have a couple of different strategies for coming up with drugs, the first of which is basically the luck approach.  Penicillin, for instance, is one of the most important drugs ever developed.  It was discovered mostly by accident.  We just stumbled on some moldy lab equipment that happened to prevent bacteria from growing and then we were like, what if I put that in my mouth? While modern drug discovery has become more sophisticated in the centuries since, it still involves a lot of maximizing the odds that we'll bump into something that works.  In one technique, called high-throughput screening, scientists just throw hundreds of thousands of different chemicals at diseased cells with the hope of finding one that works.

 (02:00) to (04:00)


Our other strategies involve a bit more rational design, which is to say, designing drugs to target specific proteins. Rational design started to come into its own in the 90s with IMATINIB. It's a drug that targets a leukemia protein that binds ATP, the molecule our cells use to store and transfer energy. Knowing this information, scientists made a library of chemicals that looked similar to ATP, and tested a bunch to see if any of them turned the protein off.

IMATINIB did. This was huge. We had designed a drug with a specific molecular target in mind.

It created hope that we could do this for all kinds of diseases. Unfortunately, the success of IMATINIB was unique for a couple reasons. First, the type of leukemia IMATINIB treats is caused by a single faulty protein.

Most diseases don't have such a simple fix. And second, we knew from prior research that IMATINIB's molecular target binds ATP. That was a huge advantage, because it gave us a starting point for IMATINIB's design.

But we don't know that for most diseases, which is why we have a vested interest in knowing what a protein will bind to, which brings us to another strategy. Structure-based design. There's a few reasons why knowing a protein's structure is important for developing medicine.

First, it gives us an idea of what might bind there, and that narrows down the otherwise massive list of chemicals we would have to test. The second is specificity. Ideally, a new drug will target specific proteins without hurting other, similar proteins.

So, how do we figure out what proteins look like? One popular method is x-ray crystallography. It involves crystallizing a protein, shooting that crystal with an x-ray beam, and then converting the pattern of light it creates into a map of the protein's atoms.

Another is NMR spectroscopy, which involves putting the protein in a massive magnetic instrument instead, making some quantum measurements and converting those into a protein structure.

 (04:00) to (06:00)


Once you have that structure, you can start rationally screening drugs, but both of these techniques have significant drawbacks. NMR only really works for smaller than average proteins. Meanwhile, x-ray crystallography is limited to proteins we can crystallize, which is not all of them as any structural biologist will complain to you. Not only are they limited in scope, but both are slow.

Solving one structure can take someone years from start to finish. Both techniques have been in use for decades, and so far we have only solved the structures for a tiny fraction of all human proteins. A third method, though, called cryo-electron microscopy, has shown a lot of improvement in recent years, but it's also limited.

It begs the question- do we have any other options? Actually, we do. Predicting the structure of proteins.

Protein structure prediction is when you convert a gene from our DNA into a protein sequence, and then feed that sequence into a computer algorithm to predict the structure the protein would fold into. This would let us skip the lag of x-ray crystallography and NMR, and gives us structures the other techniques couldn't give us. But there are still some limitations to doing everything computationally.

Research shows that if two proteins share as little as 35% of their sequence, their structures can still be about 80% similar. In other words, if we find a protein structure with an even moderately similar sequence, then we can have a pretty good idea of what a new protein will look like. But unfortunately, those minor structural differences are essential.

They're what make each protein unique. So while predicting how a protein generally folds can be easy, predicting how a protein folds specifically is very hard. Even harder is solving the structure of a protein whose sequence is unlike any other protein.

That requires a technique called de novo prediction.

 (06:00) to (07:43)


The most widely used method uses software that breaks the proteins down into pieces, finds the most stable version of each, and then sort of Frankensteins them back together. This technique is fast, but not yet as reliably accurate as x-ray crystallography and NMR. But the good news is, it's getting better every year. And that opens the door to designing more drugs that work exactly how we need them to.

For this reason, solving the protein folding problem has garnered a reputation as a sort of holy grail of biology, because it would revolutionize how we produce medicine and treat disease. If you like staying on top of science and technology, you're in the right place right now, but if you need even more videos, you might like Curiosity Stream. Curiosity Stream is a subscription streaming service that offers over 2400 documentaries and non-fiction titles from some of the world's best filmmakers, including exclusive originals, and their content features names from David Attenborough to Jane Goodall to Sigourney Weaver.

You might like their Breakthrough video collection, which tracks recent developments in science and technology, like how earth recovers from mass extinctions, or how to take a picture of a black hole. You can get unlimited access to Curiosity Stream and their collection of videos covering science, nature, history, and more starting at just $2.99 a month. For SciShow viewers, the first 31 days are completely free if you sign up at curiositystream.com/scishow and use the promo code scishow during the signup process.