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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.

If they are independent the residues in one of the networks mutate in sync with each other, but the residues from the other network mutate in a different rate. For example, If you compare two subfamilies the positions of one network all have a different residue in each of the two subfamilies (so they mutate between the two subfamilies), and the positions of the other network might be 100% conserved over the two subfamilies.

You can exploit explore this idea by making an alignment with two groups of proteins. Each group should contain functionally the same similar proteins but this function differs between the groups. For instance, a group can be made with enzymes all converting the same substrate, and a second group that convert another substrate. The correlated mutations then often reveal hotspots that determine the substrate specificity change. A very important note is that correlated mutations reflect functional changes and not environmental changes. This can easily be explained: You cannot use correlated mutations to find hotspots for thermostability. You could make an alignment of two groups: one composed of proteins from thermophilic organisms and another made from non-thermophilic organisms. Then the differences between these groups should show up as correlated mutations, right? This is not the case, because in fact these two groups cover all sequences of the complete superfamily alignment. You can investigate the differences between these groups just by comparing the amino acid contents between the groups. This concept will be discussed later. One more important note: The problem with functional grouping is that the functional annotations of proteins is often based only on sequence similarity and can therefore be wrong. You need to make sure that at least a high percentage of the sequences share the same function to make functional based hotspot finding work. There are examples though were the alignment was successfully grouped simply by using keyword searches in the protein descriptions and used to find novel enzymes. The group of Prof Uwe Bornscheuer has found R-selective amine transferases (Only S-selective enzymes where known) by deleting from the protein family groups of proteins that had other activities based on motifs that were associated with the different functions. This exercise was repeated with 3DM. The protein family was first grouped by giving keywords that were related to enzymes with unwanted reaction mechanism. The keywords lyase, for instance, was used to select of enzymes with likely the unwanted lyase activity. Then, this set of sequences was used to find a sequence motif specific for lyase activity. All sequences that had this lyase specific sequence motif were subsequently deleted from the alignment. This step ensures that all lyases that were not annotated as such still get deleted from the alignment. This procedure was repeated for all different unwanted enzyme activities. This exercise resulted in 42 sequences that most likely are all R-selective amine transferases.

If set on 2 then 3DM will only show positions in the network for which at least two different mutations have been published that had an effect on specificity. This is to ensure no falls positive false positives are included. In protein families for which a lot of mutations are available it is usually smart to leave this at at least two. If there is not so much data available it might be better to set it on 1 to get reliable E-scores.

They have the same number using the class A numbering scheme: 3.25a. In the class B alignment this residue has number 3.39B29b. Obviously there are more structural conserved positions before this residue in the class B alignment compared to the class A alignment. When this common numbering scheme was designed they didn't realize that they could have made a numbering scheme that could be applied to all classes. As everyone uses this numbering scheme now, it is too late to synchronize the numbering schemes of the different families.

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: 

1. Introducing prolines at positions where a proline is common and your target doesn't have a proline
2. Inserting negative charges at the N side of a helix (if there isn't any). Or a positive residue at the C-side. This is known as helix capping because the N terminus of a helix is slightly positive and the C-terminus is slightly negative.
3. Creating salt-bridges by inserting positive or negative residues at positions on the outside of the protein. Always check if the residue you want to use is actually common in the alignment at that position.
4. Replacing glycines where, according to the alignment, glycines can be replaced by something else.

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.