For this exercise, you need either Pymol or /wiki/spaces/DOC/pages/2854387713 installed that has the 3DM plugin. If you don't have Yasara or Pymol or you are missing the 3DM functionality, please consult the installation instructions. Before you start this exercise make sure you have the latest version of Yasara or Pymol installed.
Login at 3DM with your 3DM account. If you don't have a 3DM account you can request one via the "get 3DM" tab. To be able to do this course you need to have access to the databases used in this course. After you have requested an account you can request access to the course databases by sending an email to firstname.lastname@example.org.
After entering the login details you will see a 'Select 3DM system' page. Click on the 'Public 3DM Systems' checkbox and search for the "Phosphoenolpyruvate mutase/Isocitrate lyase" database. Open this 3DM system.
At the starting page of each 3DM database, you see the 3DM data cycle. The icons in the circle represent links to the most important 3DM options. These options are also available on the left.
Fungi can be pathogenic to plants and animals. It is known that the secretion of oxalate by fungi is a commonly used strategy for their pathogenicity. Oxalate is toxic and can form crystals that demolish the cell wall of the host. The oxalate is produced from oxaloacetate catalyzed by the enzyme oxaloacetate hydrolase (OAH). This is the reaction:
Fig 1. Reaction mechanism that produces oxalate.
We have generated a 3DM for the corresponding protein family. OAH falls in the Phosphoenolpyruvate mutase/Isocitrate lyase superfamily.
The OAH of niger is the best characterized OAH protein. This is the sequence:
For each protein in the 3DM database, there is a "protein information" page that contains more detailed information.
In the quick search (you can find box this just above the green bar in 3DM) you can use "G3Y473” or you can simply search for G3Y473 in the keyword search tab.
On the protein information pages, you can find a couple of different tabs. Have a quick look at what you can find in each tab.
3DM offers several ways to select a subset of sequences. Once a subset is selected a mini 3DM can be generated for this subset. All 3DM functionalities, such as the correlated mutations, are regenerated and can separately be analyzed. The data of a subset can also be compared to the data of the full set of sequences or with other previously defined subsets.
With the search option we have made a subset called "oxalate producers" that contains the proteins available in this 3DM system for fungi of which it is known that they can produce oxalate:
In the keyword search tab of the search option, you can select species. Here you can type the species names. We have separately searched for each of the species and the resulting proteins were added to the subset window by clicking the + signs of the subset window. Try to search yourself for Aspergillus clavatus in the species search options. You will find two proteins (A1CFP3 and A1CMM8). These are the first two proteins of the subset. You can get a list of the proteins that are in a subset by clicking in the subset window on the number (in this case 33) that indicates how many proteins are in the subset.
A subset is a mini 3DM made of only the sequences that are defined in the subset. All features and data types are re-calculated and can be compared to data in other subsets of to the whole superfamily. Here we want to find the proteins/residues responsible for oxalate production in oxalate-producing fungi. We try to find things (e.g. residues) that are specific to the subset.
At the alignment, statistics pages change between the full dataset and the "oxalate producers" you just made using the 'Subset' menu in the header on top of 3DM and see how the graphs change.
3DM always generates an extra histogram for each subset that shows the residues that are specifically conserved in the selected subset (the histogram called "subset specific conserved residues"). The highest scoring residues are around 3D positions 157.
Important here is to realize that these are positions that are not just simply conserved in this subset of oxalate producing fungi, but the corresponding residues are absent from the rest of the sequences in the superfamily. In other words, these residues are specific for this subset.
You can see this by comparing this plot with the amino acid conservation plot of the new subset. Use the "custom plot" tab and select your subset from the left box and from the right box "amino acid conservation " and "subset specific conserved residues".
You can put the slider bar of the amino acid conservation plot on 100%. You will see that there are 47 positions 100% conserved in the oxalate producers subset (see figure).
This figure shows what you should have selected. Here the conservation cut-off was set at 100%. Clearly, the subset-specific conserved residues are found mainly around position 157.
On the alignment page:
- Click on the consensus sequence at position 157
S157 → 100% in the subset.
S157 → 1.06% in the full alignment.
Difference = 98.94%. In the "subset specific conserved" plot the number is 99,38 (you can see this by putting your mouse over the peak of the subset specific conserved plot at position 157). The difference between these numbers comes from the fact that in the full alignment the serine's of the oxalate producers subset are included. The "subset specific conserved residues" plot calculates the difference between the conservation in the subset minus the conservation of the full set but then without the sequences of the subset.
Take home message: the data you are looking at is always depending on the subset tab that is selected.
Click on "Correlated mutations" in the menu on the left. Make sure you have the "Full Dataset" selected at the top of 3DM.
Correlated mutations calculated for a superfamily alignment often reflect positions important for specificity because superfamily alignments contain enzymes with different specificities (do you understand this concept?)
The proteins in a superfamily usually form groups of different specificities. Within a group the residues important for specificity are conserved, but between the groups, they mutate. Since they all mutate simultaneously between the groups they result in a correlated mutation network. Note: which protein feature is behind a correlated mutation network heavily depends on the input alignment. If you make a subset of enzymes that all have the same specificity the correlated mutation will, of course, not reflect changes in specificity and thus the network will not be composed of specificity hotspots. The concept of how to choose the input alignment is explained in more detail later in this practical.
The "Top Correlation Heatmap" page shows the alignment positions of which the residues mutate simultaneously (definition of a correlated mutation).
Position 157 is the highest correlating position. Note that you can click on the heatmap. This will lead to plots showing the amino acid distribution of the two corresponding positions. Those plots show the co-occurrence of amino acids. This data can be use to see if it might be better to make certain double mutants instead of single mutants.
- Select the "Correlation Networks" tab. Here you can give a keyword in the "Literature & Mutations" window on the right.
- Type " specificity" in the box. This will select mutations from the literature that affect specificity reported in any of the proteins of the superfamily.
It enables to the plot of any other data type of which the 3D number is known on the network. This is one of the strong features of 3DM. All data and all tools are connected via 3D numbers.
Giving the keyword specificity in the search box results in 5 papers of position 157.
The enrichment score is the factor that shows how many more times mutations related to the keyword are found in the network compared to positions outside the network. The enrichment score for specificity in the OAH network is 7.39. This means that there are 7.39 times more mutations affecting specificity published at positions in the network. Thus it is likely that specificity is the evolutionary pressure that caused the positions in the network to mutate simultaneously.
Note that an enrichment score of >4 or 5 normally is significant.
- Go back to the alignment statistics page.
- Now compare the ligand contact plot (in this case these will be enzyme inhibitors) of the full dataset with the correlation plot of the full dataset
Position 157 scores highest when both numbers are added. Positions that both make a contact with a ligand but also show correlated mutation behavior are likely hotspots for specificity.
Note: This is how you have to think when you use 3DM. If two independent data types (here ligand contact data and correlated mutation data) show a correlation then you have probably found something meaningful. When you find a correlation between data types like the above your alarm bells should start ringing. When you use 3DM you should think like this: What is my biological question? How can I use 3DM to answer this question? To answer this you should try to answer this:
- 1. What subset(s) should I generate (sometimes you can simply use the full database)?
- 2. What data do I need to compare?
Some positions, like 116 make a lot of contacts with ligands but do not show correlated mutation behavior. Do you understand why this is? Position 116 is a conserved position. This position is important for the general function of the protein (the reaction) and not for the specific function.
Note: Protein residues can, sort of, be divided into three groups.
- 1. The conserved residues (they perform the general function of the proteins)
- 2. The correlated mutations (they perform the specific function of the proteins).
- 3. The highly variable positions. These positions can often be mutated without loss of function (those you should target if you want to change stability).
Homology modeling of OAH niger with its substrate oxaloacetate and the design of an inhibitor
Build a homology model
- Go back to the protein information page of G3Y473 and select the ‘MODELS' tab.
3DM selected three structures as potential good templates. In this course, you will learn how to select the best template, make the best alignments, etc., but for now, we will use 3LYEA → it does have the best resolution (e.g. quality).
Load an inhibitor
Structures can be loaded directly in Yasara from the 3DM database via the 3DM → Structures → load structure from 3DM option. Loading structure files via the 3DM menu ensures that the structures are all superimposed, co-crystallized compounds will have to be positioned in the active site and proteins will have the 3D numbering.
- Load the inhibitor of 1M1BA using the "load data from 3DM" option in Yasara.
Build oxaloacetate from this inhibitor.
Fig 2. Structure of oxaloacetate.
This is the structure of oxaloacetate. We are very lucky since it is very similar to the structure of the 1M1BA inhibitor. Simply swapping the SO3 group with a CO2 group will do the job.
- To do this delete one oxygen of SO3 → select it and press delete.
- Then right-click on the S and select "swap → atom" and replace it with carbon. The angles are not perfect (it needs energy minimization), but it gives a quick and dirty idea how oxaloacetate fits in the active site.
- Load the 1M1B structure
- Zoom in on the ligand, find the SO3 group
- Ctrl + Middle click on one of the Oxygen atoms on the SO3 group. A number of extra objects appear in the object list on the right.
- In the command line at the top, enter: remove pk1 and press enter. The oxygen atom will disappear.
- Ctrl + Middle-click on the S atom in the group.
- In the command line, enter: alter pk1,elem="C" , then press Enter
- In the command line, enter: alter pk1,name="C4" , then press Enter
- In the object list, click on the C that appears next to the 1M1B object. Select any of the coloring schemes under Color... By element. The SO3 group will now be colored the same as a CO2 group.
The reaction mechanism of isocitrate lyase (ICL) is known for quite a while (fig 3). In this reaction mechanism the H of the blue OH group donates an electron, makes a double bond, and splits of the COOH group.
No, it has a =O instead of an OH.
Fig 3. Reaction mechanism of ICL (above) and the structure of oxaloacetate (below)
Actually, oxaloacetate in water is in equilibrium with its diol form (figure 4).
Fig 4. Oxaloacetate is in equilibrium with its diol.
Yes the diol form of OAH has the required OH group.
Until today OAH is the only known enzyme of this superfamily that has a substrate in a diol form. So the extra OH is unique to OAH.
The OH unique to OAH is sticking right towards ser157.
Because oxaloacetate is the only substrate that has this diol form the Ser contacting this extra OH is unique to OAH.
Modeling the extra OH in the active site with the "swap" option does not work very well in yasara, because yasara can't deal with changing the double bond of C=O to the single bond of C-OH without proper energy minimization (try to make the diol with the swap option if you like).
Fig 5. The result of energy minimization performed on the diol form of oxaloacetate in the OAH model.
In 2008 a model of OAH was generated similar to the way you did it today. With this model we were already in 2008 able to:
- Reveal the OAH specific serine 157 (figure 6)
- Reveal the reaction mechanism of OAH (via the diol substrate)
- Show the relation between oxalate production and pathogenicity of fungi
- Make a very strong inhibitor of OAH (potential anti-fungal drug)
The inhibitor was designed by organic chemists that realized they had to make a compound that is 100% in the diol form. This was the case with difluoro-oxaloaceate. This compound indeed proved to be a very strong inhibitor of OAH and was later crystallized together with OAH of the fungus Cryphonectria Parasitica (pdb file 3M0JA).
- To see how well you modeled oxaloacetate in the active site load the drug of 3M0JA in your model with the 3DM option of Yasara.
Fig 6. Picture of the model of OAH taken from the 2008 publication: Identification of fungal oxaloacetate hydrolyase within the isocitrate lyase/PEP mutase enzyme superfamily using a sequence marker-based method. This picture clearly shows the predicted Ser157 H-bridge with the diol of oxaloacetate.
Position 157 is the center of the correlated mutation network. P is the most common residue at position 157 (is that correct?). We have generated a subset of sequences that have a P at position 157 called "P157"
No, in a subset with only P on position 157 the P is, of course, 100% conserved and therefore can't mutate together with other positions. The correlated mutation data that 3DM calculates is a measure of how often mutations occur together between two positions. No mutations will result in a score of 0.
In the figure, you can see where the correlated mutation is found in the P157 alignment. This plot was generated by clicking "visualize all notes" in CorNet when the P157 subset was selected.
From within Yasara you can also make this visualization by the following functions:
- 3DM → select subset → P157
- 3DM → show superfamily data → Correlations
Now to answer the question do the following:
- 3DM → select dataset → full dataset
- 3DM → show superfamily data → Correlations
You can switch between the two scenes using the different tabs. Clearly, the two scenes overlap. So the correlated mutations in the P157 alignment are formed during evolution in the dimerization domain.
Note that the input alignment is a very important factor in what protein feature is behind the correlated mutation data. Many different protein features can be the evolutionary pressure resulting in correlated mutations (activity, specificity, binding to something else, enantioselectivity, and many others). Often the literature can be used to find which feature this is. This is why the enrichment score was designed.
Take home message: The function underlying correlated mutations heavily depend on the input alignment. Always look for additional data (in this case protein-protein interaction data → did you find that?) that might explain a correlated mutation network.
The correlated mutations in this superfamily seem to reflect positions important for specificity. You want to change the specificity of OAH and you decide to rationally design a mutant library. Your screening method allows you to screen up to 1000 mutant clones.
This question can be answered thoroughly or just very simple. One thing is for sure, everything shows that the correlated mutations are important for specificity. Those are your first-choice hotspots. Then pick as many positions as your screen allows only using common residues at these hotspots (e.g. residues > 2% or so). This cut-off percentage is depending on how many hotspots you want to use. The more hotpots the higher this percentage or your library size gets too big. So it is always a trade-off between the number of hotspots and the number of residues per hotspot. There are many things to consider when you choose hotspots and the residues at the hotspots. Each position should be considered carefully and all data at each position should be investigated.
These are things to consider:
- Read the articles that describe mutations at the hotspots. They might have a role that you don't want to target. The template sequence might also have things specific for the template that should not be touched.
- For the same reasons look at the structure. Am I not destroying a salt-bridge unique to the template. If so, you probably need to mutate the other partner of the salt-bridge as well. Although I would probably not touch the bridge and choose another hotspot if this position is not THE hotspot.
- Look at which proteins have the residue that I want to introduce. So for each position you look for each residue what type of protein has this amino acid. When choosing the residues to which you want to mutate you sometimes might want to exclude residues that are just above your cut-off. You might want to exclude hydrophilic residues, for instance, if those are only present in enzymes that do an exotic reaction. Try to downsize your library this way by excluding residues per position.
- Check all data types at each hotspot you want to target to see if it doesn't have a role (e.g. dimerization) that might harm the activity.
- Always use your brain to find the best combination of hotspots and amino acids. It is a tricky business and you will get better by practice.
- Make the library bigger than you can screen. If correctly designed your library will contain more than one hit and you don't need to find all hits. Even if it does contain just one hit, you also don't need to screen 98% of your library to find the hit. Screening 70% should give you enough confidence that if no improvement is detected you are better of designing a new library.