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

<css> table.colBasic2 { margin-left: auto; margin-right: auto; border: 2px solid black; border-collapse:collapse; width: 70%; } .colBasic2 th,td { padding: 3px; border: 2px solid black; } .colBasic2 td { text-align:left; } .colBasic2 tr th { background-color:#efefef; color: black;} .colBasic2 tr:first-child th { background-color:#adceff; color:black;} </css>

<figtable id="Modeller single">

Table 1:Template structures and their sequence identity to the target, as computed by Blast. The RMSD and TM score 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
		TM score	RMSD	TM score	RMSD	TM score	RMSD
1QVO_A	39%	0.6241	3.647	0.5653	4.994
1S7X_A	29%	0.3355	15.806	0.2509	18.099
1CD1_A	21%	0.3640	18.066	0.4697	5.640

</figtable>

<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. The RMSD and TM score are given for all models. The TM score ranges in the interval of (0, 1]. A value below 0.17 indicates a random similarity and a TM score above 0.5 corresponds to two structures with the same fold in CATH or SCOP (see TM score). 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	0.39	3huj_C	0.23	1QVO_A	0.39
1zag_A	0.36	1cd1_A	0.21	1cd1_A	0.21
1rjz_D	0.34	1VZY_A	0.14	-

</figtable>