Homology Based Structure Predictions Hemochromatosis

Hemochromatosis>>Task 4: Homology based structure predictions

Riddle of the task

After endless battles and deadly traps you have finally reached the tomb's final chamber. As you enter it you notice that there is no sign of the treasures that were promised by the old map you found months ago. Suddenly you hear a loud noise behind you and a solid wall of stone blocks the only entrance into the room. You are trapped! You look around and notice something on the walls. On the left wall are four runes in an ancient language. Luckily its the same language as the notes on the map you deciphered and they translate into four single letters:

• C N O I

On the opposite wall you can see four simple symbols:

• a triangle
• a square
• a circle
• and a diamond (dt. Raute)

After further investigation you notice that the four symbols can be pushed into the wall, but you don't know what would happen and which one(s) to push.

What do you do?

Some hints:

• You only have to push one button and only once.

Short Task Description

Detailed description: Homology based structure predictions

Protocol

A protocol with a description of the data acquisition and other scripts used for this task is available here.

PDB templates

In order to find templates for our models we performed several searches for homologs with COMA and HHPred. We also reused the sequences from Task 2. However none of these methods yielded homologs with a sequence identity above 40% (except 1a6z which is HFE itself) that could be mapped to a PDB structure. The best results for COMA and HHPred are listed in <xr id="coma_t"/> and <xr id="hhpred_t"/> respectively. Therefore we could not generate models with a sequence identity above 80% and the 40%-80% range was limited to its lower bound. In addition to those shown below, we also used 2iad_B (P01921, 21.10% identity) from task 2.

<figtable id="coma_t">

Table 1: Top 10 results (based on e-Value) from the COMA search sorted by Identities.

PDB ID	e-Value	Identities	Positives
1a6z_A	1.00E-63	100%	100%
1t7v_A	1.20E-63	34%	62%
3nwm_A	1.60E-57	30%	57%
1frt_A	4.90E-65	28%	66%
2wy3_A	2.80E-63	26%	66%
3ov6_A	2.70E-55	20%	67%
1u58_A	1.00E-54	19%	59%
3d2u_A	7.50E-60	16%	70%
3dbx_A	9.70E-59	16%	71%
3it8_D	3.40E-52	15%	65%

</figtable>

<figtable id="hhpred_t">

Table 2: Top 10 results (based on e-Value) from the HHPred search sorted by Identities.

PDB ID	e-Value	Identities	Similarity
1a6z_A	1.80E-69	100%	1.623
1k5n_A	2.80E-68	40%	0.725
1s7q_A	5.80E-78	37%	0.655
1t7v_A	7.40E-68	36%	0.702
3p73_A	1.10E-69	35%	0.638
3bev_A	1.00E-69	34%	0.684
2yf1_A	2.40E-74	32%	0.617
1zs8_A	7.30E-68	30%	0.553
2wy3_A	3.30E-68	29%	0.496
1cd1_A	1.60E-67	21%	0.394

</figtable>

Models

For our models we selected 1k5nA, 1zs8A, 2iadB, and 3dbxA as templates. We chose them to have a wide variety of sequence identities. The MSA models were created with a subset of these 4 templates: MSA1 contains all four templates, MSA2 the lower three (1zs8A, 2iadB, and 3dbxA), and MSA3 only 2iadB and 3dbxA. For the Modeller models we used both alignment methods (simple and 2d) to create the single template models. For the evaluation we compared the models to the "native" (complexed with beta-2-microglobulin only) and complex (complexed with beta-2-microglobulin and transferrin receptor) structure of HFE, 1a6zA and 1de4A respectively. Both structures only contain 275 of the 348 residues of HFE (residues 23-297), thus excluding the signal peptide and the transmembrane and cytoplasmic region.

Modeller

In the following segment the models were evaluated that were built with modeller. The modelbuilding was based on different alignments, created with modeller itself and valuated against our HFE protein (1A6Z and 1DE4).

The presented pictures show the resulting models (in green) superimposed with the 1A6Z protein (red). The yellow lines indicate which positions PYMol had aligned to superimpose them.

1k5nA

<figure id="1K5NbasedModels">

model built by using 1K5N, simple modeller alignment, superimposed with 1A6Z

model built by using 1K5N, 2d modeller alignment, superimposed with 1A6Z

</figure>

1zs8A

<figure id="1ZS8basedModels">

model built by using 1ZS8, simple modeller alignment, superimposed with 1A6Z

model built by using 1ZS8, 2d modeller alignment, superimposed with 1A6Z

</figure>

2iadB

<figure id="2IADbasedModels">

model built by using 2IAD, simple modeller alignment, superimposed with 1A6Z

model built by using 2IAD, 2d modeller alignment, superimposed with 1A6Z

model built by using 2IAD, simple modeller alignment, superimposed with 1A6Z

model built by using 2IAD, 2d modeller alignment, superimposed with 1A6Z

</figure>

3dbxA

<figure id="3DBXbasedModels">

model built by using 3DBX, simple modeller alignment, superimposed with 1A6Z

model built by using 3DBX, 2d modeller alignment, superimposed with 1A6Z

</figure>

MSA1

<figure id="MSA1basedModel">

model built by using MSA1 superimposed with 1A6Z

</figure>

MSA2

<figure id="MSA2basedModel">

model built by using MSA2 superimposed with 1A6Z

</figure>

MSA3

<figure id="MSA3basedModel">

model built by using MSA3 superimposed with 1A6Z

model built by using MSA3 superimposed with 1A6Z

</figure>

Evaluation

<figtable id="modeller_scores_native">

TODO: Scoring results for the models against 1a6z (native). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP..

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
1K5N_2d	272	0.1686	0.0846	0.0487	0.83298	2.200 (over 272 atoms)
1K5N_simple	272	0.1649	0.0846	0.0506	0.83358	2.325 (over 272 atoms)
1ZS8_2d	272	0.1698	0.0827	0.0432	0.85494	1.642 (over 272 atoms)
1ZS8_simple	272	0.1550	0.0790	0.0423	0.79841	2.302 (over 271 atoms)
2IAD_2d	272	0.1725	0.0836	0.0460	0.49103	2.166 (over 269 atoms)
2IAD_simple	272	0.1337	0.0607	0.0349	0.40162	3.705 (over 272 atoms)
3DBX_2d	272	0.1742	0.0892	0.0496	0.81698	2.374 (over 267 atoms)
3DBX_simple	272	0.1684	0.0882	0.0496	0.86512	1.524 (over 267 atoms)
MSA1	272	0.1680	0.0855	0.0496	0.83014	2.366 (over 270 atoms)
MSA2	272	0.3218	0.1811	0.0855	0.72682	1.889 (over 270 atoms)
MSA3	272	0.1530	0.0708	0.0358	0.40265	3.679 (over 272 atoms)

</figtable>

<figtable id="modeller_scores_complex">

TODO: Scoring results for the models against 1de4 (complex). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP..

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
1K5N_2d	272	0.1674	0.0800	0.0469	0.85238	1.986 (over 272 atoms)
1K5N_simple	272	0.1638	0.0800	0.0478	0.83836	2.142 (over 272 atoms)
1ZS8_2d	272	0.1715	0.0827	0.0450	0.86026	1.720 (over 272 atoms)
1ZS8_simple	272	0.1550	0.0790	0.0414	0.82124	2.059 (over 271 atoms)
2IAD_2d	272	0.1706	0.0836	0.0441	0.48549	2.279 (over 269 atoms)
2IAD_simple	272	0.1318	0.0653	0.0358	0.40088	3.655 (over 272 atoms)
3DBX_2d	272	0.1737	0.0873	0.0487	0.83662	2.088 (over 269 atoms)
3DBX_simple	272	0.1687	0.0901	0.0515	0.86877	1.492 (over 269 atoms)
MSA1	272	0.1698	0.0873	0.0524	0.84313	2.150 (over 270 atoms)
MSA2	272	0.3223	0.1783	0.0846	0.70465	2.011 (over 270 atoms)
MSA3	272	0.1574	0.0754	0.0377	0.40026	3.637 (over 272 atoms)

</figtable>

One can see in both figures and tables (tm-align scores) that all models built via modeller (except the one based on 2IAD) lead to fairly good models. Also it seems that using multiple templates does not mean that the resulting models get better. This is indicated e. g. between the tm-align scores of MSA2 and MSA3 and the predicted structure of their models. It is plausible that the aligned sequence of 2IAD in the MSAs leads in case of MSA3 to a disruption of the modelpositions, but in case of MSA2 or MSA1 this one template is not enough anymore to alter the resulting model greatly. Unfortunately the sequences we used have a big overlap in the MSA, it would be informative how modelling performs when different fragments were modelled from different templates.

SwissModel

1k5nA

<figtable id="swiss_1k5nA_stats">

TODO Summarized output from SwissModel for 1k5nA.

	SwissModel 1k5nA (red) superimposed on 1a6zA (green).	QMEAN and anolea distribution for SwissModel 1k5nA.
	Per residue error rate for SwissModel 1k5nA.	QMEAN statistics for SwissModel 1k5nA.

</figtable>

The alignment from SwissModel for 1k5nA spans the residues 26 to 299 of HFE. It has a sequence identity of 38.71% and contains almost no gaps. The estimated QMEAN Z-score is -1.92. <xr id="swiss_1k5nA_stats"/> shows the summary of the quality assessment provided by SwissModel. The error rates per residue fluctuate rapidly with peaks of over 4 anstrom. The anolea estimation shows two long regions with unfavorable scores from residue 69 to 110 and 128 to 206.

1zs8A

<figtable id="swiss_1zs8A_stats">

TODO Summarized output from SwissModel for 1zs8A.

	SwissModel 1zs8A (red) superimposed on 1a6zA (green).	QMEAN and anolea distribution for SwissModel 1zs8A.
	Per residue error rate for SwissModel 1zs8A.	QMEAN statistics for SwissModel 1zs8A.

</figtable>

Similar to 1k5nA the alignment spans from 27 to 298 with a sequence identity of 29.56%. The QMEAN Z-score of -2.88 is worse than that for 1k5nA which isn't surprising given the drop of 9% sequence identity. The error rates are also worse with a maximum of 8 angstrom and they almost never go below 1 angstrom. The anolea graph exhibits the same unfavorable regions as in 1k5nA, though the first region is even worse and the second a bit shorter.

2iadB

<figtable id="swiss_2iadB_stats">

TODO Summarized output from SwissModel for 2iadB.

	SwissModel 2iadB (red) superimposed on 1a6zA (green).	QMEAN and anolea distribution for SwissModel 2iadB.
	Per residue error rate for SwissModel 2iadB.	QMEAN statistics for SwissModel 2iadB.

</figtable>

In contrast to the other three templates, the alignment for 2iadB is rather short (111 to 299). This and the sequence identity of only 21.76% might be the cause for the very bad QMEAN Z-score of -3.42. The error rates don't go as high as for 1zs8A, but they never drop below 2 angstrom for the first half of the alignment and improve only slightly in the second half. This is also reflected in the anolea distribution as there are almost no favorable regions during the first half. The second half (starting around 222) gets much better which correlates with the regions from the other templates.

3dbxA

<figtable id="swiss_3dbxA_stats">

TODO Summarized output from SwissModel for 3dbxA.

	SwissModel 3dbxA (red) superimposed on 1a6zA (green).	QMEAN and anolea distribution for SwissModel 3dbxA.
	Per residue error rate for SwissModel 3dbxA.	QMEAN statistics for SwissModel 3dbxA.

</figtable>

3dbxA has the longest alignment of all templates with a length of 275 residues (24 to 298 in HFE), but it also has the lowest sequence identity with only 18.93 percent. Nevertheless it has a QMEAN Z-score of -2.8 which is even slightly better than for 1zs8A. The error rates average around 3 angstrom for the first half of the alignment and slightly improve for the second. The anolea graph again shows the two unfavorable regions from 1k5nA and 1zs8A, though this time the second one is almost neutral and the first is slightly longer.

Evaluation

TM-Score again seems to have problems to correctly align both sequences and therefore provides no meaningful data (TM-Score, GDT-TS, and GDT-HA in <xr id="swiss_scores_native"/>). When comparing the TM-Score from TM-Align to the corresponding scores for the Modeller results you can see that SwissModel does neither perform better nor worse than Modeller on a general basis.

Surprisingly 3dbxA is provides the best model (TM-Align 0.89) despite its low sequence identity and outperforms those from Modeller. Especially the weighted RMSD of 1.1 is quite good compared to the other models. In the case of 1zs8A SwissModel performs better than the simple alignment based Modeller model, but worse than the 2d alignment one. Regarding the weighted RMSD SwissModel outperforms Modeller for both alignment methods. 2iadB again performs worst (cf. Modeller results) as it suffers from the short alignment with HFE. Even with TM-Align the TM-Score barely reaches 0.5 which is the minimum threshold for similar folds. Despite its high sequence identity 1k5nA performs about as well as 1zs8A regarding the TM-Score, but has a much worse weighted RMSD.

<figtable id="swiss_scores_native">

TODO: Scoring results for the models against 1a6z (native). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP.

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
1k5nA	250	0.1626	0.0809	0.0478	0.84456	2.121 (over 272 atoms)
1zs8A	249	0.1449	0.0662	0.0377	0.83755	1.514 (over 271 atoms)
2iadB	165	0.1218	0.0680	0.0450	0.50849	2.805 (over 187 atoms)
3dbxA	252	0.1684	0.0836	0.0478	0.89308	1.111 (over 272 atoms)

</figtable>

<figtable id="swiss_scores_complex">

TODO: Scoring results for the models against 1de4 (complex). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP..

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
1k5nA	250	0.1611	0.0781	0.0469	0.85087	2.009 (over 272 atoms)
1zs8A	249	0.1450	0.0689	0.0377	0.83904	1.501 (over 271 atoms)
2iadB	165	0.1201	0.0671	0.0432	0.49068	3.172 (over 187 atoms)
3dbxA	252	0.1679	0.0818	0.0460	0.88762	1.203 (over 272 atoms)

</figtable>

I-Tasser

For the I-Tasser models we used the option to provide a template. As templates we have used so far 1k5nA and 2iadB. Sadly the process for 1k5nA is still running (submission date 2012-05-29), but 2iadB was recently finished. As there is a limit to only one submission per account and considering the long runtime, we won't be able to process more templates until tuesday. It should also be noted that I-Tasser incorporated 1a6z and 1de4 into the modelling process, both of which are pdb entries for HFE.

2iadB

<figure id="itasser_2iadB_1">

I-Tasser 2iadB (red) superimposed on 1a6zA (green).

</figure>

<figure id="itasser_swiss_2iadB_1">

I-Tasser 2iadB (red) and SwissModel 2iadB (pink) superimposed on 1a6zA (green).

</figure>

I-Tasser provided 5 models for 2iadB. The best one with a C-score of -1.46 was selected for the evaluation.

Compared to all other models this one looks the worst (see <xr id="itasser_2iadB_1"/>). A whole helix (upper front in the figure) is not predicted. This is especially weird as this helix is quite good modelled in the SwissModel for 2iadB (<xr id="itasser_swiss_2iadB_1"/>).
On the other hand I-Tasser correctly predicted the transmembrane helix around 307 to 330 (lower right corner in the figure) which is not included in the pdb file for 1a6z. I-Tasser also predicted a helix at the beginning of HFE in the signal peptide region, but this region is also not contained in the pdb file nor is it specified as a helix in uniprot.

Evaluation

TM-Align calculates a TM-Score of around 0.79 for the best model for 2iadB. Although this is much higher than the other methods achieved for 2iadB it shows that 2iadB seems to be a quite bad template for HFE as even the incorporation of the pdb structures of HFE (1a6z, 1de4) in the modelling process didn't raise the score above many of the other models based on low identity templates.

<figtable id="iTasser_scores">

TODO: Scoring results for the models against 1a6z (native) and 1de4 (complex). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP.

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
2iadB (vs. native)	272	0.1699	0.0873	0.0506	0.78679	2.333 (over 270 atoms)
2iadB (vs. complex)	272	0.1692	0.0855	0.0478	0.79875	2.161 (over 272 atoms)

</figtable>

3D-Jigsaw

The first attempt with 3D-Jigsaw contained the following models as those seemed to be about the best:

• 1zs8A (Modeller, 2d align)
• 3dbxA (Modeller, simple align)
• 3dbxA (SwissModel)
• MSA1 (Modeller)
• MSA2 (Modeller)

3D-Jigsaw failed to generate new models with these.

The second attempt contained all 15 models from Modeller and SwissModel combined. This time 3D-Jigsaw was able to generate 5 new models.

Models

<figure id="3dJigsawAll">

Jigsaw models superimposed on 1a6zA. Model1 (red), Model2 (blue), Model3 (yellow), Model4 (pink), and Model5 (cyan).

</figure>

All models seem to be almost identically (see <xr id="3dJigsawAll"/>). They also match the pdb structure (1a6zA) quite well. The only differences for the models are around the coiled regions not included in the pdb structure (residues 1-22 and 298-348). This is no surprise as all previous models also had the problem that the templates lacked these regions.

Evaluation

It is not surprising that all 5 models have about the same scores as they almost look alike. Although they can compete with the best models from the other methods none of them are really astonishing. The TM-Score (TM-Align) and weighted RMSD are both lower than several of the provided models (e.g. 3dbxA).

<figtable id="scores_native">

TODO: Scoring results for the models against 1a6z (native). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP..

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
model_1	272	0.1647	0.0864	0.0533	0.83038	2.359 (over 272 atoms)
model_2	272	0.1648	0.0873	0.0542	0.83125	2.351 (over 272 atoms)
model_3	272	0.1648	0.0873	0.0542	0.83127	2.350 (over 272 atoms)
model_4	272	0.1648	0.0873	0.0542	0.83127	2.350 (over 272 atoms)
model_5	272	0.1648	0.0873	0.0542	0.83127	2.350 (over 272 atoms)

</figtable>

<figtable id="scores_complex">

TODO: Scoring results for the models against 1de4 (complex). Common residues, TM-Score, GDT-TS, and GDT-HA are calculated by TM-Score. TM-Align is the TM-Score based on TM-Align. Weighted RMSD calculated with SAP..

Model	Common residues	TM-Score	GDT-TS	GDT-HA	TM-Align	Weighted RMSD
model_1	272	0.1637	0.0836	0.0506	0.83550	2.166 (over 272 atoms)
model_2	272	0.1637	0.0836	0.0506	0.83622	2.158 (over 272 atoms)
model_3	272	0.1637	0.0836	0.0506	0.83627	2.157 (over 272 atoms)
model_4	272	0.1637	0.0836	0.0506	0.83625	2.158 (over 272 atoms)
model_5	272	0.1637	0.0836	0.0506	0.83626	2.157 (over 272 atoms)

</figtable>

Conclusion

TODO: make text :P

• HFE structure more conserved than sequence
• low identity =/= bad model
• alignment important
• correlation