Mix and Match: The Numbers behind Combinatorial Chemistry

Combinatorial chemistry is not so much a field as it is a methodology for conducting scientific research.  The key element of these types of techniques is developing a large library of different combinations of molecular parts.  After that, you’ll need to develop a set of screening criteria and then test your library against those criteria.  In some ways, getting a true positive “hit” in a library requires a bit of luck.  This is partially why these techniques are sometimes looked down upon by people in fields such as structural biology.  I, however, think that they have tremendous value because in performing experiments this way not only will your data give you a “best hit” but the spread of those data can tell you something about the underlying assumptions of your screen.

As part of my graduate work I designed and developed a screen for peptides that would activate only under a certain set of conditions.  I could have done this by, for example, creating a peptide, testing it, and then make modifications to it and test again.  This is a more, “rational design” approach and it’s a completely valid method.  I’d invite you to think about the drawbacks of this method. How do I know how many times would I need to alter the peptide until it was perfect?  Do I even know if my starting peptide is the right place to start?  Instead of struggling with these questions I decided to utilize a “combinatorial method”.  I synthesized a library of peptides that had varying controlled combinations of amino acids.  The total number was over 10,000 different peptides yet the synthesis took only about two weeks!  I was able to separate and test each peptide individually and I get a set of data similar to the one shown below (Figure 1).  By setting the conditions X and Y I could find the peptides that had the max X response and the max Y response.  These were my “positive hits”.

Good data

Figure 1 (Example of a “good” screen)

The spread of the data tells me how stringent my screening conditions are.  If the conditions are too easy then no peptides are “selected for” in other words the data would look a lot like Figure 2.  Can we really say whether or not any one of those dots is significantly different from any of the others?  Of course, if the conditions are too harsh such that all the dots are zero then I won’t see any “positive hits” either.  One other piece of information that can get overlooked is what the stringency tells us about the system our molecules live in.  If X and Y are model biological conditions, perhaps these data suggest that our model is not close enough to reality.  Alternatively, perhaps it means that the molecule we were already trying to force to evolve is already close to ideal.  I think because of this it is shortsighted to say that combinatorial assays cannot address basic scientific questions.

Confusing Data

Figure 2 (example of confusing data)

I hope to cover a whole host of different combinatorial assays in this blog.  Not only is it a useful technique for peptides but also for studying the “yoga” of other biomolecules such as polymers and DNA.  Additionally, these screens can end up being used in combination with advanced genetics techniques.  If you have a question about combinatorial chemistry or a cool example please feel free to post it here for discussion! Thanks for reading!

Sincerely,

GRW

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Structure-Function Relationships for Drug Discovery

Good afternoon everyone!  I wanted to expound upon our last post by describing one of the main paradigms in biology and chemistry: that a molecules structure relates to its function.  From the last post I threw some doubt on this topic with a thought experiment that showed structures are not always optimized to a specific function.  Despite this, there are many instances in nature where a molecule fits perfectly into a specific target.  This could be, for instance, like the Biotin-Streptavidin bond we discussed before.  In fact, mimicking molecular shapes is a very important part of drug discovery.

Distinct protein shapes are often important targets for drug therapies.  Enzymes, those proteins that catalyze chemical processes, often have a particular binding pocket for a target.  Sometimes the target is a key component of a malignant microorganism, like the outer coating of a virus, bacterium, or fungus.  I have recently been studying a class of anti-fungal drugs that work by inhibiting an enzyme that produces a key fungal coat product: ergosterol.  These drugs were developed by using ergosterol as a “lead compound”.  Scientists started out by modifying ergosterol to develop molecules that get stuck in the enzymes inner binding pocket (see figure 1).  Because of its precise shape, the drug is able to form bonds with certain amino acids in the enzyme and it is limited in its ability to escape.  These types of drug discovery methods highlight why crystal structures of proteins are vitally important to drug companies.

 

Drug Inhibition

 

Adapted from:

https://en.wikipedia.org/wiki/Fluconazole#/media/File:Fluconazole_ball-and-stick_model.png

https://en.wikipedia.org/wiki/Lanosterol#/media/File:Lanosterol_molecule_ball.png

 

Even though drug molecules can be designed with high affinity for specific molecules, this does not always completely define its function.  Ergosterol, for example, has a common analogue in mammalian cells: cholesterol.  For this reason, there can be some crossover between drugs that bind to ergosterol makers and drugs that bind to cholesterol makers.  Such crossover might lead to off-target, negative effects for humans.  This is an active area of research as of the time of writing this article. All of this is just one example of the challenges and concerns in developing effective inhibitor-drugs.  I will talk about lead compound development and how to develop them into effective drug compounds in future posts.  Thanks for reading!

Sincerely,

GRW