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. 3luy as well as  plus the new 2qmx and 2qmw fall into the ACT-domain-group as mentioned at the HHPreddataset
Comparison of datasets
In summary one can say, that the approaches all find similar results for the dataset with 80% sequence similarity or above. Those are all structures of Phenylalaninehydroxylase with different modifications or from different organisms. But only Coma and HHPred find the same (1phz) structure, whereas PDBeXplore finds a completely different identifier, which is the protein with its co-substrate.
Most differences occur in the second dataset, in which PDBeXplore finds a lot of possible candidates with a very good e-Value range. But the other two do only find two or even no result at all.
But in the third datset (<30% identity) PDBeXplore finds some candidates, but the e-Values are to high too be considered good hits. In contrary to HHPred and Coma which both found good hits with a low e-Value as well as identity in the desired range.
What also might have been observed by an interested reader, are the differences in identity and e-Value throughout the supposed to be identical hits, like 3luy. We are not entirely sure where these might arise, but since the difference is not that significant we expect them to descend from different alignment scores and or weighting.
IN this part we create homology models with different methods, in order to examine the structure of our unknown protein.
Here we will show you the models one can gain from Modeller <ref name="modeller">A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993. </ref>. We used a local version at home.
Modller offers mainly two possibilities:
- single template modelling
- multi-template modelling
We are going to show you the differences and possibilities this offers.
In this part you choose one sequence which you believe is the closest relative to your target sequence and model with the alignment of those two. As we did some dataset creation before, the choice of sequences is already done. We now only have to use two scripts to first align and then actually model this pairwise alignment. Then it will be assessed with DOPE-score and GA341<ref name="GA341">Francisco Melo, Roberto Sánchez, Andrej Sali; Protein science : a publication of the Protein Society, Vol. 11, No. 2. (February 2002), pp. 430-448, doi:10.1002/pro.110430</ref><ref name="DOPE">Min-yi Shen and Andrej Sali Protein Sci. 2006 November; 15(11): 2507–2524.doi: 10.1110/ps.062416606</ref>.