Structure-based mutation analysis HEXA

Sequence Description

We had to use a [PDB] file, in which are no missing residues and the quality of the structure should be high. We found only one PDB structure which was not bounded to a ligand. Therefore, we could not regard the quality and the pH value, the R-factor and the coverage. Nevertheless, we listed in the following table this values:

experiment type	X-Ray diffraction
Resolution	2.8 Å
temperature (Kelvin)	100K
temperature (Celsius)	-173 °C
pH-Value	5.5 (slightly acid)
R-Value	0.270

It was not possible to find one file, without any missing residues. In each file there was a gap between residue 74 to 89 and the last amino acid. Therefore, we decided to cut off the first 89 residues and use a PDB file with a structure from 89 - 528. This file can be found [here].

Mutations

Because of the shorten PDB file, it was not possible for us to analyse the first two mutations on position 29 and 39.

SNP-id	codon number	mutation codon	mutation triplet
rs4777505	29	Asn -> Ser	AAC -> AGC
rs121907979	39	Leu -> Arg	CTT -> CGT
rs61731240	179	His -> Asp	CAT -> GAT
rs121907974	211	Phe -> Ser	TTC -> TCC
rs61747114	248	Leu -> Phe	CTT -> TTT
rs1054374	293	Ser -> Ile	AGT -> ATT
rs121907967	329	Trp -> TER	TGG -> TAG
rs1800430	399	Asn -> Asp	AAC -> GAC
rs121907982	436	Ile -> Val	ATA -> GTA
rs121907968	485	Trp -> Arg	gTGG -> CGG

Analysis of the mutations

We created for each mutation an extra page. The summary of the analysis can be seen in the Summary Section.

• [rs61731240: His -> Asp]

• [rs121907974: Phe -> Ser]

• [rs61747114: Leu -> Phe]

• [rs1054374: Ser -> Ile]

• [rs121907967: Trp -> TER]

• [rs1800430: Asn -> Asp]

• [rs121907982: Ile -> Val]

• [rs121907968: Trp -> Arg]

Protocol - Using the methods

PyMol

We visualized the local hydrogen-bonding network with following commands:

distance hbonds, all, all, 3.2, mode=2
zoom resi <interval>
hide labels, all
color red, resi <mutation_position>

Furthermore, we also used the polar contact mode in PyMol to visualize the H-bonds.

The clashed are visualized by the following commands:

distance clash = pos_mutation, all, 2.0, 0
zoom clash

FoldX

To use FoldX, we created a runfile, which can be found [here]. We fitted the temperature and pH-value to the values we extracted from the [PDB] page. Furthermore, we analysed the mutations with a random chosen temperature and pH value, to see how much influence these parameters have on the analysis.

We ran FoldX with following command:

FoldX -runfile run.txt > foldx_output

minimise

Next we used minimise. Therefore, it was not necessary to create any file for the run. Sadly, we could not find any documentation about minimise and therefore, it was really hard to figure out how it works and what means the output.

We ran minimise with following command:

minimise <input> <output>

Gromacs

Before we could run Gromacs, we had to curate our PDB file. Therefore, we used the script repairPDB to extract chain A. Next we run SCRWL to make sure, that every residue is available in the PDB file.

Then we used the commands which are listed in our task section.

Additionally, to the analysis of our mutated sequences, we also chose different forcefields and analysed our protein without any mutation with these forcefields. Here are the results of this analysis:

analysed energies (in kJ/mol)		force field
		AMBER3	AMBER99SB-ILDN	CHARMM27
Bond	Average	852.968	1091.57	1796.6
	Err Estimation	-	270	240
	RMSD	42.0241	-nan	2924.29
	Drift	-74.0853	-1622.75	-1404.11
Angle	Average	3438.47	3326.81	4764.7
	Error Estimation	-	62	60
	RMSD	16.8864	-nan	466.82
	Drift	-33.7041	404.076	368.45
Potential	Average	-50917.7	-61304.1	166.582
	Error Estimation	-	960	39
	RMSD	66.4149	-nan	79.7058
	Drift	-132.636	-6402.44	280.841

Furthermore, we used different numbers for nsteps. The result of how long these analysis run, can be found in the following table and graph:

nstep	time real	time user	time sys	#steps
50	8.268s	3.860s	0.110s	24
500	27.523s	47.650s	0.540s	321
5000	25.281s	17.710s	0.210s	114
50000	14.940s	14.210s	0.300s	91

Results

Energy

In the following table we list all different energy values which are calculated by the different programs. To get the possibility to compare the values with each other we also calculated a ratio between the value of the wildtype and the mutation and compared these two values. It is not possible to compare the values of the different programs directly, because each program use other assumptions for the calculation of the energy. Nevertheless, we think, that the general trend between these programs has to be similar and therefore, we calculated the ratio to compare them.

	FoldX		Minimise		Gromacs
Mutation	energy value	Ratio difference	energy value	Ratio difference	energy value	Ratio difference
wildtype	-154.17	0	-9610.467157	0	-61304.1	0
Rs61731240	-151.61	1.56	-9480.968602	1.35	-48160.5	21.44
Rs121907974	-144.25	6.34	-9594.637506	0.16	-46177.4	24.68
Rs61747114	-153.78	0.15	-9606.588566	0.04	-48802.5	20.40
Rs1054374	-152.15	1.21	-6189.246312	35.60	-48652.6	20.64
Rs121907967	-	-	-	-	-	-
Rs1800430	-155.11	-0.72	-9505.864181	1.09	-48693.9	20.57
Rs121907982	-154.36	-0.23	-9618.062763	-0.08	-48418.9	21.02
Rs121907968	-149.08	3.20	-9608.663976	0.02	-49443.8	19.35

FoldX

There are differences in the energy of the mutated structures and the wild-type structure, but in general these differences are not that strong. The highest energy values have mutation Rs121907974 and Rs121907968, where the mutated structures have 6% and 3% more energy than the wild type. The lowest difference is between wildtype and Rs61747114 with only 0.15, which is nearly 0. There are two structures which have less energy than the wildtype (mutation Rs1800430 and Rs121907982) but also here the difference is very low (0.23 and 0.72) and therefore it is hard to explain why these mutations damage the protein, because the energy difference is not that high.

Minimise

Minimise has lower energy values than FoldX. But these two values are not comparable, because both methods based on different calculation models. In this case Rs1054374 has the highest energy different with about 35%, which is very high. The lowest energy differences can be seen for mutation Rs61747114 (0.04) and Rs121907968 (0.02). There is also one structure which has a higher energy, but the value is very low (-0.08). In general expect the mutation Rs1054374 almost all of the other structures has less energy difference compared to the wildtype.

Gromacs

Gromacs uses the lowest values, but as before, these values are not comparable with the other methods because of different calculation models. In this case you can see a strong difference between wildtype and mutation with about 20% in average. Furthermore, there is no big difference between the values of the different mutations, as we could seen before. The lowest energy difference has mutation Rs121907968 with 19.35 whereas the highest differences is between wildtype and mutation Rs121907974 with 24.6. This result is consistent with FoldX, where Rs121907974 also has the highest difference between wildtype and mutation.

Discussion

In this section we want to discuss the results of the structure-based mutation analysis.

General

Interestingly, Gromacs do not calculate any structure with lower energy than the wildtype. FoldX and Minimise have almost always the same trend. There are two exception with the mutation Rs1054374 which has a low ratio with FoldX and a very high ratio with Minimise. Furthermore, FoldX calculated a higher energy than the wildtype energy for Rs1800430, whereas Minimise does not predict this trend. In general to compare the predictions of the different programs we draw a graph with the different values which can be seen in Figure 1.

As you can see in the picture with some exceptions, all three programs predict the same energy trend, if we calculate the ratio and not compare the total values. Therefore, if there is a strong difference between wildtype and mutation all three methods predict it mostly correct and we can conclude, that these mutation cause damages on the protein structure and function.

Bonds

Mutation	Position	H-bonds original	H-bonds mutated
Rs61731240	179	no	no
rs121907974	211	no	no
rs61747114	248	no	no
rs1054374	293	two	no
rs121907967	329	-	-
rs1800430	399	no	no
rs121907982	436	no	no
rs121907968	485	one	one (different location)

Additionally to the energy calculation we also had a look to the H-bonds between the amino acid we looked at and the rest of the protein. In most cases the amino acid does not have any H-bonds. Only in two cases there are H-bonds. In the first case (rs1054374) the wildtype has 2 H-bonds, whereas the mutated structure do not have any H-bonds. Therefore we suggest, that this mutation destroy the protein function. The second case is mutation rs121907968 where both (mutation and wildtype) have one H-bond. But in this case the mutated amino acid has a wrong H-bond. That is the reason why we decided that this mutation damages the protein.

Conclusion

Now we want to decide if one mutation damage the protein. This decision is made because of the H-bond analysis and the analysis of the energy values. If two methods predict the same trend, and the third another trend, we decided to trust the majority. We classify a mutation as damaging if there are changes in the H-bonds or the ratio between the wildtype and mutation energy is more than 1.

Mutation	damaging	decision because of
Rs61731240	yes	FoldX, Minimise, Gromacs
Rs121907974	yes	FoldX, Gromacs
Rs61747114	no	FoldX, Minimise
Rs1054374	yes	Minimise, Gromacs, H-bonds
Rs121907967	yes	shorter chain
Rs1800430	yes	Minimise, Gromacs
Rs121907982	no	FoldX, Minimise
Rs121907968	yes	FoldX, Gromacs, H-bonds

Because we already know if the mutation is damaging or not, we compare our conclusion with the reality.

Mutation	database	effect	prediction	comparions
Rs61731240	SNP-DB	neutral	non-neutral	wrong
Rs121907974	HGMD, SNP-DB	non-neutral	non-neutral	right
Rs61747114	SNP-DB	neutral	neutral	right
Rs1054374	SNP-DB	neutral	non-neutral	wrong
Rs121907967	HGMD, SNP-DB	non-neutral	non-neutral	right
Rs1800430	SNP-DB	neutral	non-neutral	wrong
Rs121907982	SNP-DB	neutral	neutral	right
Rs121907968	SNP-DB, HGMD	non-neutral	non-neutral	right

In the last table we can see that about 70% of our predictions are right, whereas 30% are wrong. Therefore this is a bad result and we can see, that analysing the energy of a mutated structure alone is not sufficient to decide if a mutation is neutral or non-neutral.