Structure-Based Mutation Analysis Hemochromatosis

Hemochromatosis>>Task 7: Structure-based mutation analysis

Riddle of the task

It took you over an hour to figure out the right combination, but the door is finally open. The sight is unbelievable. Inside the nex room lies a treasure beyond imagination: heaps of gems, gold, and jewelry. Exotic furs, marvelous paintings, and many more. You step inside to collect what should now be yours...

The moment you reach out for the first piece of treasure it vanishes into thin air. ALL of it. The treasure was just an illusion... You look around and see another entrance into the room. A collapsed one. Across the room is a person, kneeling before another door. You shout... No answer. He didn't even move. As you get closer to him you see that, whoever it was, is dead. His skin mummified due to the dry air. Next to him an old leathery backpack. You reach out to take it as you notice small fragments on the floor. They look like tiny bits of red glass. Now that you're in front of him you also see many of these splinters burried inside the person's flesh. Within the backpack you find several glass orbs: a blue one, a yellow one, a green one, an orange one, a cyan one, and a violet one. Infront of the dead man, at the bottom of the door, you notice three slots. Each of them about the size of the orbs. One of them is red, the second one orange, and the third one yellow...

Short task description

Detailed description: Structure-based mutation analysis

Protocol

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

Structure selection and mapping of the mutations

<figure id="mut_map">

Figure 1: M35T, V53M, G93R, Q127H, A162S, L183P, T217I, R224W, E277K, and C282S mapped onto 1a6zC. Mutations are shown in sticks representation and colored red. Glycosylation sites are colored cyan. Disulfide bonds are colored orange and also shown as sticks.

</figure>

There are only two structures available for HFE at PDB: 1a6z and 1de4. We chose 1a6z for this task as it has the better resolution (2.6 Å instead of 2.8 Å) and has only a beta-2-microglobulin in addition to HFE. In 1de4 HFE would be complexed with transferrin receptor (TFR). All of the mutations from the previous task (M35T, V53M, G93R, Q127H, A162S, L183P, T217I, R224W, E277K, and C282S) are included in the PDB structure (residues 26-297).

<xr id="mut_map"/> shows a three dimensional mapping of the mutations (red) onto 1a6zC. Glycosylation sites (cyan) and disulfide bonds (orange) are also indicated. The only such residue that is directly affected by a mutation is the disulfide bond spanned by C225 and C282 where C282 is mutated into Serine. Though Q127H, L183P, and R224W are quite close to the glycosylation site at 130 and the two disulfide bonds (C124-C187, C224-C282) and therefore might affect them indirectly.

SCWRL and FoldX

M35T

<figtable id="M35T_pymol">

Table TODO:.

	SCWRL.	FoldX.
	SCWRL.	FoldX.

</figtable>

V53M

<figtable id="V53M_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

G93R

<figtable id="G93R_pymol">

Table TODO:.

	SCWRL.	FoldX.
	SCWRL.	FoldX.

</figtable>

Q127H

<figtable id="Q127H_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

A162S

<figtable id="A162S_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

L183P

<figtable id="L183P_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

T217I

<figtable id="T217I_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

R224W

<figtable id="R224W_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

E277K

<figtable id="E277K_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

C282S

<figtable id="C282S_pymol">

Table TODO:.

	SCWRL.	FoldX.

</figtable>

Minimise

Next we used Minimise to minimize (lame pun...) the energy for each of the 31 models created with SCWRL (10 mutations + WT) and FoldX (10 mutations and wildtypes). Each model was consecutively minimized five times (i.e. the output from the previous iteration was used as input for the next one). A table with the absolute energy values can be found here.

The median energy change per iteration is shown in <xr id="energy_gain"/>. It clearly demonstrates that too many iterations not only fail to improve the model, but make it even worse. For the FoldX models only the second iteration makes the models better, every iteration thereafter makes the models worse than they were after the first one. The SCWRL models stop to improve after the third iteration. After the fifth iteration they are about as good as after the first iteration.

<figtable id="energy_gain">

Table TODO: Median energy change per iteration of minimization. Each box is based on the energy difference between the current and the first iteration. Statistics are shown for all 31 models (left), all 20 FoldX models (center), and all 11 SCWRL models (right).

	All models.	FoldX models.	SCWRL models.

</figtable>

Gromacs

title = PBSA minimization in vacuum
cpp = /usr/bin/cpp
define = -DFLEXIBLE -DPOSRES
implicit_solvent = GBSA
integrator = steep
emtol = 1.0
nsteps = 500
nstenergy = 1
energygrps = System
ns_type = grid
coulombtype = cut-off
rcoulomb = 1.0
rvdw	 = 1.0
constraints = none
pbc = no

We used this .mdp file for evaluating all energies. For more information regarding the arguments read this.

Conclusion

Maybe?

References

<references/>