Structure-based mutation analysis Gaucher Disease

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Revision as of 16:43, 25 June 2012 by Zhangg (talk | contribs) (Runtime analysis)

The aim of this task was to carry out a thorough analysis of ten mutations and to classify them as disease-causing and non-disease causing. Technical details are reported in our protocol.

Cystral structure

<figtable id="tab:mutations">

PDB Res [Å] R value Coverage pH
2nt0 1.80 0.18 96% (40-536) 4.5
3gxi 1.84 0.19 96% (40-536) 5.5
2v3f 1.95 0.15 96% (40-536) 6.5
2v3d 1.96 0.16 96% (40-536) 6.5
1ogs 2.00 0.18 96% (40-536) 4.6

The 5 crystral structures of glycosylceramidase with the highest resolution. The physiological lysosomal pH value is 4.5. 2nt0 was selected for the analysis. </figtable>

Mutations

<figtable id="tab:mutations">

Nr Pos
P04062
Pos
2nt0_A
From To Disease
causing
1 99 60 H R No
2 211 172 V I No
3 150 111 E K Yes
4 236 197 L P Yes
5 248 209 W R Yes
6 509 470 L P No
7 351 312 W C Yes
8 423 384 A D Yes
9 482 443 D N No
10 83 44 R S No

Mutations used for the structure-based mutation analysis. </figtable>

<figure id="fig:mutations">

2nt0_A with the selected mutations used for the structure-based analysis. Blue: wildtype residues; Red: mutant residues; Orange: active site residues E235 and E340.

</figure>

SCWRL

We employed SCWRL <ref name="scwrl">Qiang Wang, Adrian A. Canutescu, and Roland L. Dunbrack, Jr.(2008). SCWRL and MolIDE: Computer programs for side-chain conformation prediction and homology modeling. Nat Protoc.</ref> for substituting the wildtype residues listed in <xr id="tab:mutations"/> by the corresponding mutatant residues which are chosen from a rotamer library. <xr id="fig:scwrl"/> denotes the results.

<figure id="fig:scwrl">

Rotamers of SNPs from <xr id="tab:mutations"/>. Blue: wildtype; Red: rotamer SCWRL; In brackets: energy(mutant)-energy(wildtype). </figure>

None of rotamers chosen by SCWRL clashed with another side-chain or the backbone. The only mutation which led to a structural change was L470P. Here, the insertion of proline interrupted the beta-sheet. The hydrogen bonding network changed in case of mutation number 1, 5, 7, and 8 (cf. <xr id="tab:scwrl"/>). W209R introduces a hydrophilic arginine which forms a hydrogen bond to T180. Although not predicted by SCWRL, the arginine might impact the protein structure. W312C is located next to the active site (cf. <xr id="fig:mutations"/>) and there exists a hydrogen bond to E340. Substitution the hydrophobic tryptohphane by a hydrophlic cysteine in the vicinity of the active site might account for the disease-causing effect of this mutation.

As expected, all mutations increased the energy of the model (cf. the energy difference in brackets in <xr id="fig:scwrl"/>). The energy increased most in case of L470P due to the break of the beta-sheet. A384D and W209R also made the model less stable which is caused by substituting an unpolar residue by a charged residue. All four mutations which increased the model energy most are disease-causing.

<figtable id="tab:scwrl">

Nr Mutation Wildtype Mutatant Clashes Structural
change
H-bonds Hydrophobicity H-bonds Hydrophobicity
1 H60R T471 Hydrophilic G62 Hydrophilic No No
2 V172I Hydrophobic Hydrophobic No No
3 E111K Hydrophilic Hydrophilic No No
4 L197P Hydrophobic Hydrophobic No No
5 W209R Hydrophobic T180 Hydrophilic No No
6 L470P T482 Hydrophobic T482 Hydrophobic No Yes
7 W312C E340, C342, P316 Hydrophobic E340, C342 Hydrophilic No No
8 A384D Hydrophobic V404 Hydrophilic No No
9 D443N Hydrophilic Hydrophilic No No
10 R44S S13, Y487 Hydrophilic S13, Y487 Hydrophilic No No

Structure-based analysis of SNPs from <xr id="tab:mutations"/>. H-bonds: residues involved in forming hydrogen bonds (cut-off: 3.2 Å). </figtable>

We further noticed that SCRWL changed the backbone at some positions which led to different secondary structure assignments (<xr id="fig:scwrl_ss"/>). The positions at which the deviations could be observed were independent from the mutated sites.

<figure id="fig:scwrl_ss">

Seconary structure elements of 2nt0_A (grey) compared to secondary structure elements of models built by SCRWL.

</figure>

FoldX

The superposition of the rotamer configurations predicted by FoldX and SCWRL are shown in <xr id="fig:foldx"/>. The predictions of both tools differed in case of four mutations. In case of H60R, the side-chain orientation of arginine predicted by FoldX forms two instead one hydrogen bonds to T741 and might therefore impact the protein structure more than the orientation of SCRWL. In case of A384D, the romater of FoldX might be more stable than the one of SCWRL since it has a higher distance to the surrounding residues. In case of D443N we prefer the prediction of SCWRL which is closer to the wildtype configuration. For the same reason we prefer the prediction of FoldX in case of R442. For the subsequent GROMACS analysis, we hence chose the FoldX model in case of mutation number 8 and 10 and the SCWRL models for all all other mutations.

<figure id="fig:foldx">

Rotamers of SNPs from <xr id="tab:mutations"/>. Blue: wildtype; Red: rotamer SCWRL; Orange: rotamer FoldX; In brackets: energy(mutant)-energy(wildtype). </figure>

A comprehensive list of the differences between the mutant and the wildtype models can be found here. The total energy increased in case of mutation number 4-8, and 10. Just as in case of SCWRL (cf. <xr id="fig:scwrl"/>), L470P, A384D, and W209R increased the energy of the model most. Since it is unlikely that mutations like V172I decrease the energy, we consider the energy calculations of SCWRL as more plausible.

Minimise

SCWRL models

1: H60R

<figure id="fig:minimise_scwrl_e">

Energy of the SCWRL mutant models compared to the SCWRL wildtype models over five iterations minimise. </figure>

<figure id="fig:minimise_scwrl_m">

Side-chain conformation of the SCWRL mutant models compared to the SCWRL wildtype models over five iterations Minimise. </figure>

FoldX models

<figure id="fig:minimise_foldx_mutations">

Side-chain optimization of FoldX models over five iterations minimise. Green: the input model. </figure>

Gromacs

Runtime analysis

To show the relationship between nsteps and runtime of 'mdrun', different nstep were chosen from 50 to 5000. Three different energy functions were selected:

  1. AMBER03 protein, nucleic AMBER94
  2. CHARMM27 all-atom force field (with CMAP)
  3. OPLS-AA/L all-atom force field

<figure id="fig:runtim">

Runtime of minimization with Gromacs for different nsteps by using the OPLS-AA/L force field and the wildtype structure 2nt0_A.

</figure>


In <xr id="fig:runtim"/> we showed the runtime plot for different setting of nstep(from 50 to 5000). By using the OPLS-AA/L force field, the program needed 1177 steps to reach the minimum. From nstep=50 to nstep=1177, the runtime increased lineraly because it would run exactly nstep steps. After nstep=1177, since the minimum has reached, the program would always stopped at 1177 steps no matter what the setting of nstep was, therefore the running time stayed in the same.

Mutations

Mutation 1


Energy                      Average   Err.Est.       RMSD  Tot-Drift
-------------------------------------------------------------------------------
Bond                        1624.88        820    5492.83   -5022.62  (kJ/mol)
Angle                       4289.66         76    402.822   -411.894  (kJ/mol)
Potential                  -45289.6       2900    16896.6   -18817.6  (kJ/mol)

References

<references/>