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">

TODO: COMA: Top10 (e-Value) 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">

TODO: HHpred: Top10 (e-Value) 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.

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: NATIVE.

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: COMPLEX.

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

	TODO	TODO
	TODO	TODO

</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

	TODO	TODO
	TODO	TODO

</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

	TODO	TODO
	TODO	TODO

</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

	TODO	TODO
	TODO	TODO

</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: NATIVE.

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: COMPLEX.

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

Job still running (~10hrs)...

3D-Jigsaw

Model1

<figure id="3dJigsawModel1">

PyMol view of the 3dJigsaw created model1

</figure>

Evaluation

<figtable id="scores_native">

TODO: NATIVE.

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: COMPLEX.

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>