Task 5: Homology Modeling

lab journal task 5

1A6Z chain A was used as modeling target for all three methods.

Modeller

We used Modeller to create models based on a single template and multiple templates.

Single template

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<figtable id="Modeller single">

Table 1:Template structures and their sequence identity to the target, as computed by Blast. The RMSDs and GDT scores are given as a quality measure for the different models based on a pairwise sequence alignment with dynamic programming (std Alignment), pairwise sequence alignment with additional secondary structure information (2d Alignment) and manually curated alignments (curated Alignment).

Template	Seq. identity	std Alignment		2d alignment		curated Alignment
		RMSD	GDT score	RMSD	GDT score	RMSD	GDT score
1QVO_A	39%	3.647	0.6241	4.994	0.5653
1S7X_A	29%	15.806	0.3355	18.099	0.2509
1CD1_A	21%	18.066	0.3640	5.640	0.4697

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<xr id="Modeller single"/> lists the selected templates and the Modeller results for the different template structures and alignment methods. In addition to the standard pairwise sequence alignment based on dynamic programming, we also used Modeller's alignment.alig2dn() method to improve the alignment by including secondary structure information and improved the alignments manually. As Modeller quality score, we chose the [DOPE score], which is a statistical potential that was optimized for the assessment of model quality. The DOPE score has an arbitrary scale, but scores for structures of the same protein are comparable and can be used to select the best model from a collection of structures. The lower the score, the better the model. In addition to the DOPE score, we also computed the RMSD and GDT score. The RMSD is a a good measure of the average distance between all pairs of corresponding atoms in two structures. Therefore, the lower the RMSD the better. For the GDT score, the average coverage of the target sequence under four defined distance cutoffs is computed. Normally, 1, 2, 4 and 8 Å are used as distance thresholds. The GDT score ranges between 0 and 100, with random superpositions of unrelated structures having a score of 10 to 20.

Including the secondary structure information did only improve the model of the most distant homolog 1CD1_A.

<figtable id="pymol str. al.">

Figure 1:Superposition of the target 1A6Z_A (green), the template 1QVO_A (red) and the model (purple). Two different alignment methods were used to create the input alignment. for Modeller.

a) classical pairwise sequence alignment

b) inclusion of secondary structure information in the alignment

</figtable>

<figtable id="pymol str. al.">

Figure 2:Superposition of the target 1A6Z_A (green), the template 1S7X_A (red) and the model (purple). Two different alignment methods were used to create the input alignment. for Modeller.

a) classical pairwise sequence alignment

b) inclusion of secondary structure information in the alignment

</figtable>

<figtable id="pymol str. al.">

Figure 3:Superposition of the target 1A6Z_A (green), the template 1CD1_A (red) and the model (purple). Two different alignment methods were used to create the input alignment. for Modeller.

a) classical pairwise sequence alignment

b) inclusion of secondary structure information in the alignment

</figtable>

Multiple templates

We also user more than one template in a modeling step. Therefore, we created three sets of structures, one with close homologes, one with distant homologes and one combined set.

<figtable id="multiple sets">

Table 2: The three different sets used as templates for Modeller: two sets of close and distant homologs and a mixed set.

close homology		distant homology		mixed
Template	Seq. identity	Template	Seq. identity	Template	Seq. identity
1QVO_A	39%	3HUJ_C	23%	1QVO_A	39%
1ZAG_A	36%	1CD1_A	21%	1CD1_A	21%
1RJZ_D	34%	1VZY_A	14%

</figtable>

<xr id="multiple sets"/> specifies the three sets.

<figtable id="multiple sets">

Table 3: Results of the modeling with multiple templates. We computed models using 2 structures as templates and also using three structures.

	close homology		distant homology		mixed homology
Template	1QVO_A, 1ZAG_A	1QVO_A, 1ZAG_A, 1RJZ_D	3HUJ_C, 1CD1_A	3HUJ_C, 1CD1_A, 1VZY_A	1QVO_A, 1CD1_A
RMSD	3.432	2.431	4.130	7.741	3.974
GDT score	0.6553	0.7638	0.5607	0.3814	0.5846
Pymol visualisation	Visualisation of the target (green) and the model created from 1QVO_A and 1ZAG_A (purple).	Visualisation of the target (green) and model created from 1QVO_A, 1ZAG_A and 1RJZ_D (purple).	Visualisation of the target (green) and model model created from 3HUJ_C and 1CD1_A (purple).	Visualisation of the target (green) and model created from 3HUJ_C, 1CD1_A and 1VZY_A (purple).	Visualisation of the target (green) and the model created from 1QVO_A and 1CD1_A (purple).

</figtable>

Swiss-Model

We used Swiss-Model to create models using 1QVO_A, 1S7X_A and 1CD1_A as template.

<figtable id="swiss-model">

Table 4: .

	1QVO_A	1S7X_A	1CD1_A
Template	1QVO_A	1S7X_A	1CD1_A
Seq. identity	39%	29%	21%
RMSD	2.847	2.757	3.604
GDT score	0.6774	0.7086	0.6121
Pymol visualisation	Visualisation of the target (green), the template 1QVO_A and the model (purple).	Visualisation of the target (green), the template 1S7X-A and the model (purple).	Visualisation of the target (green), the template 1CD1_A and the model (purple).

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