A cheminformatics problem: protonate with SMILES and InChI

I am teaching a python programming course and this is one of the projects I want to try this year.

The over all goal of the project is to write one or more programs that generates protonation states for a list of nitrogen containing molecules specified by name. The project uses SMILES, and maybe InChI, which you can read more about here.

Getting started
1. Write code that from this list molecules = ["CCN", "CNC", "CN(C)C"] generates this output

CCN
CC[NH3+]

C[N@H]C
C[N@@H]C
C[NH2+]C

C[N@](C)C
C[N@@](C)C
C[N@H+](C)C
C[N@@H+](C)C

2. Write code that from this list  molecules = ["C(C(=O)O)N"] generates this output (the order is not important)

C(C(=O)O)N
C(C(=O)O)[NH3+]
C(C(=O)[O-])N
C(C(=O)[O-])[NH3+]

3. C(C(=O)O)N is the amino acid glycine.  Extend this program to work for alanine, asparagine, aspartate, and lysine.  Use this site to get SMILES strings for these amino acids. Find a picture of asparagine and make sure you're treating the side-chain correctly.

The project
4. (optional) Figure out how to generate a file containing SMILES strings from a file containing names. The best way is probably bash.  Get inspiration here, here, and here.

5. Generate all possible protonation state SMILES for the molecules in Table 2 in this paper. If you completed step 4 you can use tools like https://pdftables.com/ to generate a file with the names.

6. Repeat for Table 1 and 3

7. (optional) The neutral form of the amino acids histidine and arginine side chain groups have tautomers. Generate SMILES for all tautomers (InChI might help you identify tautomers).

8. (optional) Do any of the molecules in step 5 and 6 have tautomers? If so generate SMILES for all tautomers.

Some code snippets to get you started



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Enzyme design: My latest paper explained without the jargon

Our latest paper has just appeared in the open access journal PeerJ.  It's ultimately related to making better enzymes so first some background.

Background
Enzymes are proteins that make certain chemical reactions go faster and nearly every complex molecule in your body is made by, or broken down by, enzymes.  But people have also started using enzymes in commercial products, for example in washing powder to break down oily stains at lower temperatures. This saves money on heating the water and, being proteins, the enzymes are biodegradable. So there is a lot of interest in designing new enzymes that build or break down new molecules efficiently. For example, there is a rather large company (Novozymes) near Copenhagen that does nothing but design, produce, and sell enzymes on an industrial scale.

Designing new enzymes currently involves a lot of trial-and-error, so you have to pay a lot of smart scientists a lot of money for a long time to design new enzymes - a cost that is ultimately passed on to you and I as consumers. My long-term goal is to reduce the amount of trial-and-error by writing a computer program that can predict what changes you have to make to improve the enzyme before you ever make it in the lab.

I've had some modest success with a prototype program some years ago (you can find the papers here and here) for one enzyme.  But one of the many things we don't know is whether the method we base our approach on will work at all for other types of enzymes. The paper that just got published is a first small step in figuring this out.

The New Study
We've collected data for five other enzymes from other published papers that we trust reasonably well and tested two methods that are fast enough to design enzymes - one is the same method we used a few years ago and the other is a newer one that wasn't available to us before now.  The conclusion of our study is that the methods seem to work well enough for all but one system, and this system is different for the two methods.  This suggests that we can't just base future work on one method. We have to have both ready in case one of them fail.  We need to repeat the study for many other types of enzymes - I would say at least 15-20 more - and we need to improve the quality of the data so that we trust it completely, rather than "reasonably well".  In the paper we have extended an open invitation to other scientists to contribute to this effort.




This work is licensed under a Creative Commons Attribution 4.0