Structure-based mutation analysis GLA

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Revision as of 02:04, 5 September 2011 by Drexler (talk | contribs) (I117S (Mutation 3))

by Benjamin Drexler and Fabian Grandke

Introduction

In this task we analyse the structure of our protein to find out what effects the point mutations have. Therefore we created mutated structures and compared them to the wild-type protein. Several tools based on different methods have been used to achieve that aim. We used the mutations that we have chosen in the previous task.

Methods

In the first step of this task we had to find available protein structures for our protein and to decide which one would be the best for our detailed analysis. We set several cut-offs to exclude improper structures. The following tools have been used to perform the energy calulations. They were used as described in the task description.

SCWRL

SCWRL was initially developed by Dunbrack et al. in 1997. We use SCWRL4<ref name=dunb>G. G. Krivov, M. V. Shapovalov, and R. L. Dunbrack, Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins (2009)</ref> which was published in 2009. The program takes a PDB file and a sequence file as input. By usage of a rotamer library, collision detection, and a residue interaction graph the optimal side-chain conformation is calculated, based on the backbone and the mutated sequence given in the input files. The output is a PDB file containing the conformation and the total minimal energy of the graph in STDOUT.

FoldX

FoldX was developed by Serrano et al. in 2002<ref name=serr>Guerois R, Nielsen JE, Serrano L., Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutation. Journal of Molecular Biology (2002)</ref>. We used version FoldX 3.0 beta 4. The program provides the calculation of determination of energy effects of point mutations. It provides different run modes, but basically it takes a PDB file as input calculates several single energies(e.g. Van der Waals, Electrostatics, ...) and returns the single energies together with the total energy as output.

Minimise

Before this tool from the virtual box was used we had to remove the hydrogens and waters from the PDB file with the script repairPDB. Afterwards we were able to compare the energies differences between the wildtype and the mutated protein.

GROMACS

GROMACS is mostly a package to perform molecular dynamics, but it also provides energy calculations. For the mutations we used the forcefield AMBER03 and for the wildtype AMBER03, AMBERGS and CHARMM27. Additionally to the energy calculation task we did a runtime analysis with values from nsteps=10 to nsteps=1500. The results are shown in the results section of this task. According to the task description we created an MDP file with the following content:

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

Keyword Describtion<ref name=manual>Gromacs Manual</ref>
General
title Name of Project
cpp Location of c-preprocessor
Preprocessing
define Defines to pass to the preprocessor;
-DFLEXIBLE:include flexible water in stead of rigid water into your topology;
-DPOSRES: include posre.itp into your topology, used for position restraints
Implicit Solvent
implicit_solvent Simulation with implicit solvent using the Generalized Born formalism
Run Control
integrator Steepest descent algorithm for energy minimization
nsteps Maximum number of steps to integrate or minimize
Energy minimization
emtol Rhe minimization is converged when the maximum force is smaller than this value
Output
nstenergy Frequency to write energies to energy file
Tables
energygrps Group(s) to write to energy file
Neighbor searching
ns_type Type of neighbor searching
pbc Remove the periodicity (make molecule whole again)
Electrostatics
coulombtype Type of coulomb energy
rcoulomb Distance for the Coulomb cut-off
VDW
rvdw distance for the LJ or Buckingham cut-off
Bonds
constraints Which constraints should be used

Within the GROMACS work step we used the script fetchpdb. It checks if the given input is a valid PDB entry. If the check was successful it downloads the PDB file, extracts it and removes the packed version.


Results

Structure Selection

There are several structure files available for our protein:

PDB ID Resolution [Å] ph-Value R-Factor Coverage [%] Missing Residues
1R46 3.25 8.0 0.262 99.7 422-429
1R47 3.45 8.0 0.285 99.5 422-429
3GXN 3.01 NULL 0.239 88.08 422-429
3GXP 2.20 NULL 0.204 81.9 422-429
3GXT 2.70 NULL 0.245 97.29 422-429
3HG2 2.30 4.6 0.178 97.32 422-429
3HG3 1.90 6.5 0.167 98.64 427-435
3HG4 2.30 4.6 0.166 99.86 422-429
3HG5 2.30 4.6 0.192 100 422-429
3LX9 2.04 6.5 0.178 98.92 423-435
3LXA 3.04 6.5 0.216 99.52 427-435
3LXB 2.85 6.5 0.227 99.3 427-435
3LXC 2.35 6.5 0.186 98.31 423-435

We set two cutoffs to decide which structures are excluded:

  • ph-value: < 6.5
  • resolution: > 2.7

After we applied the cutoffs to our set of structures three were left (exclusion factors are colored red in the table). One of them was slightly better than the other ones so we decided to use 3HG3 (worse values are colored gray in the table). Additionally 3GH3 has the best overall resolution and R-factor (colored green). As the missing residues are very similar for all structures they are not further taken into account.

Visual Examination of the Mutations

Figure 1 shows the protein α-galactosidase A and the residues which will be mutated. In the following sections, we compare the side chain conformation of the mutated residues and discuss the influence of the mutation. Aspects will be, inter alia, loss of polar interactions and clashes with other residues.

SCWRL was used to model the side chain conformation of the mutated residue and we use the term tool-based to describe this side chain conformation. The side chain conformation which was done according to this tutorial is referred to as manual side chain conformation.

Figure 1: Representation of the protein α-galactosidase A. The residues which will be mutated are colored red. Asp170 and A231 are part of the active site and colored cyan. The ligand is colored green.
  • close to active site?
  • comparison of side chain conformation
    • clashes?
  • polar interactions
  • hydrophobic/hydrophilic?
  • surface

M42T (Mutation 1)

Figures 2 to 4 show the side chain conformation of the residue 42 in α-galactosidase A. The only difference between the manual and the tool-based side chain conformation is a variation in the conformation of the hydroxyl group of threonine (see figure 3 and 4). The tool-based side chain conformation does not lead to any clashes with the surrounding residues.

The wildtype M42 has two hydrogen bonds with E87 and Y88. Since these hydrogen bonds are formed by the carboxylgroup of the backbone, they are also abundant in the mutation, but T42 also forms a hydrogen bond with G85.

A part of T42 is exposed to the surface (see figure 5B). This is no problem, since threonine is slightly hydrophilic. But the mutation introduces a small hole into the surface of GLA (see figure 5A and 5B).

Figure 2: Close-up of methionine (wildtype) at position 42 in the protein GLA.
Figure 3: Close-up of threonine (mutated) at position 42 in the protein GLA. The side chain conformation was done manually.
Figure 4: Close-up of threonine (mutated) at position 42 in the protein GLA. The side chain conformation was done by SCRWL.
Figure 5: Surface representation of the protein GLA. (A) The wildtype residue methionine at position 42 is colored green. (B) The mutated residue threonine at position 42 is colored red. The mutation leads to a small hole in the surface of the protein.

S65T (Mutation 2)

The side chain conformation of the wildtype and the mutated residues is shown in figure 6 to 8. The hydroxyl and methyl group of threonine point towards totally different direction in the tool-based conformation (see figure 6 and 8), but there are no clashes with other residues. The wildtype S65 forms five hydrogen bonds with surrounding residues (C63, K67, L68, F69). The mutated residue T65 forms also five hydrogen bonds, but one of them is with E66 instead of K67.

Serine is hydrophilic and the residue is exposed on the surface, but threonine is also a hydrophilic residue. There is no remarkable change of the surface of the protein.

Figure 6: Close-up of serine (wildtype) at position 65 in the protein GLA.
Figure 7: Close-up of threonine (mutated) at position 65 in the protein GLA. The side chain conformation was done manually.
Figure 8: Close-up of threonine (mutated) at position 65 in the protein GLA. The side chain conformation was done by SCRWL.

I117S (Mutation 3)

Since the tool-based side chain conformation of the mutated residue (see figure 11) is pretty similar to the side chain conformation of the wildtype (see figure 9), there are no clashes in the mutated structure. The angle of the hydroxyl group is slightly different in the manual side chain conformation (see figure 10).

The hydroxyl group in the backbone of I117 forms two hydrogen bonds with L120 and A121. These bonds are also abundant in the mutated structure. Serine is a hydrophilic amino acid, but this is no problem, because it is part of the surface. Overall there is no remarkable difference in the surface of the protein due to the mutation.

Figure 9: Close-up of isoleucine (wildtype) at position 117 in the protein GLA.
Figure 10: Close-up of serine (mutated) at position 117 in the protein GLA. The side chain conformation was done manually.
Figure 11: Close-up of serine (mutated) at position 117 in the protein GLA. The side chain conformation was done by SCRWL.

A143T (Mutation 4)

Close-up of the wildtype residue number 143 of GLA.
Close-up of the mutated residue number 143 of GLA.
Close-up of the mutated residue number 143 of GLA.

H186R (Mutation 5)

Close-up of the wildtype residue number 186 of GLA.
Close-up of the mutated residue number 186 of GLA.
Close-up of the mutated residue number 186 of GLA.

P205T (Mutation 6)

Close-up of the wildtype residue number 205 of GLA.
Close-up of the mutated residue number 205 of GLA.
Close-up of the mutated residue number 205 of GLA.

D244H (Mutation 7)

Close-up of the wildtype residue number 244 of GLA.
Close-up of the mutated residue number 244 of GLA.
Close-up of the mutated residue number 244 of GLA.

Q283P (Mutation 8)

Close-up of the wildtype residue number 283 of GLA.
Close-up of the mutated residue number 283 of GLA.
Close-up of the mutated residue number 283 of GLA.

Q321E (Mutation 9)

Close-up of the wildtype residue number 321 of GLA.
Close-up of the mutated residue number 321 of GLA.
Close-up of the mutated residue number 321 of GLA.

R363C (Mutation 10)

Close-up of the wildtype residue number 363 of GLA.
Close-up of the mutated residue number 363 of GLA.
Close-up of the mutated residue number 363 of GLA.

Energy Comparison

The results of the energy comparison are presented in the table below. Due to the fact that the result of the first run of the eighth mutation clearly differed from the other results, the run was repeated with the outcome from the first run as input. Thus, there is the number 8.2. This observation shows that minimise has a decreased tolerance for clashes in comparison to the other tools. Their results for the eighth run are not outstanding and seem not to be affected by the fact that a proline was inserted into a helix. Furthermore, their results seem to be almost equally with respect to some variance. Only the comparison of the FoldX results of the mutations with the wildtype show, that the inserted mutations have a huge influence on the energy of the protein.

Number AA-Position Codon change Amino acid change SCWRL4 FoldX FoldX - Diff Minimise Minimise - Diff
WT - -20.93 - -20481.23 -
1 42 ATG-ACG Met -> Thr 343.25 157.29 -178.22 -20324.41 -156.82
2 65 AGT-ACG Ser -> Thr 327.798 152.87 -173.8 -20339.34 -141.89
3 117 ATT-AGT Ile -> Ser 333.027 157.97 -178.9 -20353.47 -127.76
4 143 cGCA-ACA Ala -> Thr 333.944 154.40 -175.33 -20339.32 -141.91
5 186 CAC-CGC His -> Arg 323.717 154.57 -175.5 -20321.32 -159.91
6 205 gCCT-ACT Pro -> Thr 340.619 155.96 -176.89 -20345.87 -135.36
7 244 gGAC-CAC Asp -> His 333.594 152.08 -173.01 -20393.12 -88.11
8 283 CAG-CCG Gln -> Pro 332.631 159.91 -180.84 -8027.71 -12453.52
8.2 - - - - - - -19134.48 -1346,95
9 321 tCAG-TAG Gln -> Glu 332.853 160.95 -181.88 -20246.98 -234.25
10 363 TATa-TAA Arg -> Cys 330.56 150.50 -171.43 -20295.77 -185.46

Gromacs

Figure 11: nstep vs. Elapsed Time in Gromacs.

Wildtype

Force Field Average Error Estimat RMSD Tot-Drift (kJ/mol)
Bond
AMBERGS 1826.99 420 4409.39 -2499.37
AMBER03 1639.74 410 4358.68 -2424.42
CHARMM27 2908.14 350 4779.8 -2033.44
Angle
AMBERGS 5496.47 74 476.18 408.548
AMBER03 5324.13 72 469.75 369.24
CHARMM27 7975.2 86 798.12 432.901
Potential
AMBERGS -114713 1200 5648.79 -7915.46
AMBER03 -91307.7 1200 5559.82 -7839.05
CHARMM27 136.699 32 64.3892 227.896


Mutations

Force Field Average Error Estimat RMSD Tot/Drift
Bond
1 1815.39 570 5166.85 -3384.48
2 1862.77 610 5331.85 -3618.04
3 1773.13 520 4937.34 -3068.93
4 1828.63 580 5229.18 -3479.09
5 1870.95 610 5361.67 -3713.22
6 1816.6 550 5091.81 -3303.34
7 1819.7 570 5173.34 -3397.07
8 2992.15 1700 -nan -10631.8
9 2083.16 830 -nan -4913.82
10 1867.42 620 5390.82 -3693.03
Angle
1 5183.95 85 360.959 550.303
2 5195.33 80 364.473 515.645
3 5196.5 89 353.256 586.473
4 5175.59 85 364.496 547.465
5 5113.99 81 365.511 526.244
6 5200.44 85 356.964 553.934
7 5261.77 87 365.202 565.196
8 5178.73 76 -nan 215.036
9 5201.95 76 -nan 442.141
10 5174.48 88 375.775 555.294
Potential
1 -90528.4 1600 7234.09 -10149.1
2 -90481.9 1600 7442.03 -10340
3 -90654 1500 6928.73 -9614.54
4 -90541 1600 7311.04 -10343.7
5 -91011.7 1600 7484.45 -10592.5
6 -90782.2 1600 7226.99 -10188.5
7 -90232.9 1600 7236.24 -10198
8 -87316 3600 -nan -23670.3
9 -90090.3 1900 -nan -12335.3
10 -89721.8 1700 7523.88 -10750.1


Mutation Plot
1
Gromacs energy calculation for mutation 1.
2
Gromacs energy calculation for mutation 2.
3
Gromacs energy calculation for mutation 3.
4
Gromacs energy calculation for mutation 4.
5
Gromacs energy calculation for mutation 5.
6
Gromacs energy calculation for mutation 6.
7
Gromacs energy calculation for mutation 7.
8
Gromacs energy calculation for mutation 8.
9
Gromacs energy calculation for mutation 9.
10
Gromacs energy calculation for mutation 10.

References

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