Homology-based structure prediction (PKU)
Short Task Description
After the sequence based predictions of function and secondary structure for our protein we will determine the 3D structure of the wild type protein and observe the influence one or several SNPs have on this structure. Of the variety of methods to be used for tertiary structure prediction, we choose homology modeling as a first approach to our goal. Read the complete task description here. The protocol of commands and scripts can be found in our journal
Due to our prior knowledge of the protein responsible for PKU, the evaluation of the methods applied, is easier than for a completely unknown sequence. In <xr id="fig:1pahstruct" /> one can see the monomer and the active site of Phenylalaninehydroxylase. On the other side ( <xr id="fig:2pahstruct" />) one can see the polymere in its active form which can be found in the human body.
Here we will show the steps we took building the models we then use and evaluate. In order to start the sheer model-building we first have to construct some datasets, which will be the founding of our models.
These datasets were derived from several sources. They all consist of PDB-entries, but we ensured to no include the already known structure of our protein, so we have a better insight in the topic of homology modeling with a completely unknown sequence.
For this set of datasets we used the webservice of sequence similarity search provieded by the pdb called PDBeXplore, which can be accessed here. In the used dataset (see <xr id="tab:datasetpdbe" /> we restricted the received data from pdb, such as we didnt use the structure of both the monomer and the dimer etc. We also did not use the structure with different ligands in order to keep the variability high.
In the dataset of sequences above 80% we only found one significant hit, which is the structure for Phenylalanine Hydroxilase dephosphorylated. This is a marginal case for the noninclusion of the protein itself, but we decided, since its from another organism, that we include it.
The dataset with sequenceidentity from 40% to 80% sequenceidentity only contain structures in connection with aromatic hydroxylation namely Tryptophan and Tyrosin from chicken and rat though the structure gained from the rat also contains the tetramerisation domain we also find in our reference structure. But we also found Tryptophan and Tyrosin hydroxylase structures in the pdb derived from human.
As for the lower than 30% dataset, we can not really expect to find usefull output here, because the best E-value we could find is 6.7.
The dataset with the highest sequencesimilarity in <xr id="tab:datasetHHPred"/> contains two structures with a very high similarity, with is due to the fact, that the structure is that of the original protein in different states. One is the protein in complex with Tetrahydrobiopterin (BH4), which is a co-factor for the PheOH-activity. The other is the phosphorylated proteinstructure.
In the second dataset (40%-80%) we find two of the structures which were alread explained above.
The last dataset from HHPred contains five structures which only decend from bacteria with only one of the structures has a direct connection with PheOH as this one binds L-Phe. The others all are connected or part of the ACT-domain which is known to be controlled by amino-acid concentration, which relates to our target protein.
In the above 80% dataset we find again our structure from above.
Unfortunately we did not receive any result for our second dataset.
But the choice for our third dataset was great. We chose the PheOH-counterpart from the CHROMOBACTERIUM VIOLACEUM namely 1ltu and one (2v27) from COLWELLIA PSYCHRERYTHRAEA 34H which is a version of the protein, that works in a much colder environment.
Comparison of datasets