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Note: Before doing this practice it is best to first do the practical on NR ligand binding/wiki/spaces/DOC/pages/327684, which is the introduction to 3DM. |
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A hotspot basket is nothing more than a selection of alignment positions. You can generate hotspots for different protein features (e.g. correlated mutations, specificity hotspots, thermostability hotspots, etc) and those can be found using 3DM. At a later stage you can open the basket in different 3DM tools. Let's see how this works.
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The B-factor method doesn't seem to be a good method for the GPCRs. Likely this is because the transmembrane helices are so tightly bound that automatically the rest of the protein is much more flexible. It is known that making mutations in the transmembrane helices can make GPCRs more stable. This is nicely demonstrated by the "thermostability" literature search. Clearly the two methods do not overlap indicating that the B-factor method does not apply to GPCRs. It is in this protein family probably much better to first try the positions that are already published to have an effect on the stability. The best way to do this is to find amino acids described in literature that are know known to stabilize GPCRs. If the stabilizing residue is not in your target GPCR then it might be a good idea to try that residue. If your target sequence has a different residue than the consensus it might be smart to try the consensus residue as there are several papers demonstrating that making the consensus often has a beneficial effect on the stability. (Note that if it is easy to screen your protein for thermostability than randomizing each hotspot might be smarter). There are many other tricks too. Here are some examples:
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The idea of the panel design tool is to select sequences from the alignment such that the selected sequences are maximally distributed over the superfamily. This is done in two steps: First the sequences of the superfamily are grouped. This can simply be based on sequence similarity (similar sequences are within the same group), but groups can also be based on sequence motifs found at user selected positions. The last option is used to group sequences based on a protein feature. For instance, the user can pick positions important for specificity. The idea is that sequences that have the exact same residues (the same motif) at those positions they are likely to have the same specificity. Both methods can be combined. In the second step a user defined number of sequences (usually one or two) are selected from each group. The selection step contains all kinds of options to maximize the chance that these proteins are likely to express. Lets Let's see how it works.
Select the "panel design" option in 3DM. First we will divide the super-family based on sequence motifs. Because we want maximize the specificity range in the panel, we will use the "specificity hotspot" basket you have generated in question 3130. This way all sequences with the same motif at our specificity hotspots (thus will likely have the same specificity) will be in one group. Use the "add hotspots" button to select the hotspot basket you made in Q30 (it should contain 9 positions).
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Note that sometimes you need to make a panel of a subset of sequences. For instance, say you want to find the most active enzyme with a certain specificity. Then you should first make a subset that contains only sequences that have this specificity. This sounds simple, but due to wrong notation of proteins is tricky. The best way to do this is to first do a keyword search to find enzymes likely to have the correct specificity and make a subset of this set of sequences. Then use this subset to make a motif with 4 to 7 amino acids that is specific for this subset. It is best to use the "subset specific residues" plot (consult the OAH questions about this plot). Make a new subset that contains all sequences that have this motif. With this approach you will not only find sequences with the correct specificity but that are annotated as "hypothetical protein", but you will also delete the sequences which are wrongly annotated. This approach doesn't make sure you have all sequences with the correct specificity, but it does maximise the chance that the once ones that are in your subset all indeed have the correct specificity. |
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- Select the "Manual" tab in the left box and type V117C. To add this mutation to the experiment, simply drag this mutation into the right box.
- Save the mutations by clicking "save" in the right box.
- Click on "Sequences" on the left.
- At "Maximum # of mutations per sequence" select 4 and select "fill up each sequence to contain maximum # of mutations". This will ensure that all sequences will have 4 mutations. If you don't use this option then sequences can have single, double, and triple mutations. Be sure to check the option “create demo measurement data”.
- Select 96 sequences to generate and set the minimum number of observations on to 2 and click on "convolute mutations".
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