Difference between revisions of "Rs4777505"

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(Conservation Analysis with Multiple Alignments)
(Secondary Structure Mutation Analysis)
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''' Comparison with the real Structure: '''
 
''' Comparison with the real Structure: '''
   
Afterwards we also visualize the position of the mutation (red) in the real 3D-structure of PDB (Figure 3 and Figure 4) and compare it with the predicted secondary structure. The visualisation can therefore like above the predicted secondary structure display if the mutation is in a secondary structure element or in some other regions.
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Afterwards we also visualize the position of the mutation (red) in the real 3D-structure of PDB (Figure 5 and Figure 6) and compare it with the predicted secondary structure. The visualisation can therefore like above the predicted secondary structure display if the mutation is in a secondary structure element or in some other regions.
   
 
Here in this case the mutation position agrees with the position of the predicted secondary structure and is at the end of a beta sheet. Like explained above this means a mutation will probably not destroy the whole beta sheet. Otherwise, it can cause a change of the further secondary structure element, which can result in a loss of function. We think that a structural change is more probable, because when we look at the prediction the mutation is not at the last sheet element. Therefore, it can cause some structural changes in the sheet which will have probably structural changes of the protein as a consequence and therefore can affect the protein function.
 
Here in this case the mutation position agrees with the position of the predicted secondary structure and is at the end of a beta sheet. Like explained above this means a mutation will probably not destroy the whole beta sheet. Otherwise, it can cause a change of the further secondary structure element, which can result in a loss of function. We think that a structural change is more probable, because when we look at the prediction the mutation is not at the last sheet element. Therefore, it can cause some structural changes in the sheet which will have probably structural changes of the protein as a consequence and therefore can affect the protein function.
   
 
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| [[Image:29_mut.png|thumb|250px|Figure3: Mutation at position 29]]
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| [[Image:29_mut.png|thumb|250px|Figure 5: Mutation at position 29]]
| [[Image:29_mut_detail.png|thumb|250px|Figure 4: Mutation at position 29 - detailed view]]
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| [[Image:29_mut_detail.png|thumb|250px|Figure 6: Mutation at position 29 - detailed view]]
 
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|}
   

Revision as of 16:16, 31 August 2011

General Information

SNP-ID Rs4777505
Codon Number 29
Mutation Codon Asn -> Ser
Mutation Triplet AAC -> AGC

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Pysicochemical Properties

First of all, we explored the amino acid properties and compared them of the original and the mutated amino acid. Therefore we concluded which possible effect the mutation could have on the protein.

Asn Ser consequences
polar, small, hydrophilic, negatively charged polar, tiny, hydrophilic, neutral Both amino acids are polar and hydrophilic. Ser is tiny, Asn therefore is a small amino acid. The biggest difference between these two amino acid is, that Asn is negatively charged and Ser is neutral. But this is not that big difference and therefore we suggest, that this mutation do not delete the structure and function of the protein.


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Visualization of the Mutation

In the next step, we created the visualization of the mutation with PyMol. Therefore we created a picture for the original amino acid (Figure 1), for the new mutated amino acid (Figure 2) and finally for both together in one picture whereas the mutation is white colored (Figure 3). The following pictures display that the original amino acid Asparagine has a much smaller chain than Serine. Contrary Serine is longer and comes to a fork at the end of its rest. Furthermore, it is also orientated in a different direction. Otherwise comparing to other mutations this one is not that extreme. Therefore, this exchange will probably not cause huge effects on the protein structure and function.

picture original amino acid picture mutated amino acid combined picture
Figure 1: Amino acid Asparagine
Figure 2: Amino acid Serine
Figure 3: Picture which visualize the mutation


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Substitution Matrices Values

Afterwards, we looked at the values of the substitution matrices PAM1, PAM250 and BLOSSUM62. Therefore, we looked detailed at the three values: the value for the according amino acid substitution, the most frequent value for the substitution of the examined amino acid and the rarest substitution frequency.

In this case, the substitution of Asparagine to Serine has very high values that is nearer to the values for the most frequent substitution for PAM1. Contrary for PAM250 the value for the amino acid substitution Leucine to Arginine is average. This means the most frequent substitution value is almost as far away as the rarest substitution.. The difference between the two PAMs can be ascribed to the different preparations of these two kind of substitutions matrices. For the PAM1-matrix the substitution rate is 1%, which means the probability that one amino acid changes is 1% and that there is 99% similarity. Contrary, PAM250 means that 250 mutations have been fixed per 100 residues which has as result only similarity about 20%. A possible reason that PAM250 has a better value for the amino acid substitution is that the similarity is low and the amino acids are probably dissimilar. Finally, both PAM-matrices show that this mutation is very common. BLOSOUM62 has a very bad value which corresponds to the most frequent substitution. BLOSSUM62 is based on protein families which means that this substitution is very common in some protein families. So in sum, all three substitution matrices point out that this specific substitution from Asparagine to Serine is very frequent. A possible consequence can therefore be that this mutation has no dramatical effects on the protein.


PAM 1 Pam 250 BLOSOUM 62
value aa most frequent substitution rarest substitution value aa most frequent substitution rarest substitution value aa most frequent substitution rarest substitution
20 36 (Asp) 0 (Cys, Met) 5 7 (Asp) 2 (Cys, Leu, Phe, Trp) 1 1 (Asp, His, Ser) -4 (Trp)


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PSSM Analysis

Besides, we looked additional at the position specific scoring matrix (PSSM) for our sequence. In contrast to PAM and BLOSOUM, the PSSM contains a specific substitution rate for each position in the sequence. Therefore, the PSSM is more position specific than PAM or BLOSOUM. We extracted the substitution value for the underlying mutation, the value for the most frequent substitution and the rarest substitution.

In this case the substitution rate for Asparagine to Serine at this position is high and near the value of the most frequent substitution. This means this substitution at this position is likely very common which indicates that this substitution has probably no bad effects as consequence. Therefore, we concluded that this mutation will not cause protein structure changes as well as functional changes.


PSSM
value aa most frequent substitution rarest substitution
3 4 -5


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Conservation Analysis with Multiple Alignments

As a next step we created a multiple alignment which contains the HEXA sequence and 9 other mammalian homologous sequences from [UniProt]. Afterwards, we looked at the position of the different mutations and looked at the conservation level at this position. The regarded mutation is presented by the first colored column in Figure 4. Here we can see, that the most of the other mammalians have another amino acid at this position. Only one other mammalian agreed and had an Asparagine at this position. Therefore, the mutation at this position is bad conserved and results probably in no structural and functional changes.

Figure 4: Mutation in the multiple alignment


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Secondary Structure Mutation Analysis

As a next step we compared the different results of the secondary structure prediction tools JPred and PsiPred. Afterwards, we can examine in which secondary structure element and where therein the mutation takes place. This can give an overview of how drastic the mutation can be. In this case both tools agree and predict at the position of the mutation the end of a sheet. Therefore, we can see, that the mutation at this position would not destroy or split the whole beta-sheet. It will probably only change the end of the sheet, but this can also cause a change of the the following secondary structure. This can lead to structural change of the protein which can cause a functional loss for the protein.

JPred:
CCHHHHHHHHHHHHHCCCCCCCEEEEEEEEEECCCEEEEECCCEEEEECCCCCCC...
PsiPred:
CHHHHHHHHHHHHHHHCCCCCCCCCCCCEEEECCCEEEEECCCEEEEECCCCCCC...

Comparison with the real Structure:

Afterwards we also visualize the position of the mutation (red) in the real 3D-structure of PDB (Figure 5 and Figure 6) and compare it with the predicted secondary structure. The visualisation can therefore like above the predicted secondary structure display if the mutation is in a secondary structure element or in some other regions.

Here in this case the mutation position agrees with the position of the predicted secondary structure and is at the end of a beta sheet. Like explained above this means a mutation will probably not destroy the whole beta sheet. Otherwise, it can cause a change of the further secondary structure element, which can result in a loss of function. We think that a structural change is more probable, because when we look at the prediction the mutation is not at the last sheet element. Therefore, it can cause some structural changes in the sheet which will have probably structural changes of the protein as a consequence and therefore can affect the protein function.

Figure 5: Mutation at position 29
Figure 6: Mutation at position 29 - detailed view


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SNAP Prediction

Next, we looked at the result of the SNAP prediction. For this prediction we took the amino acid of the certain position and checked every possible amino acid mutation. Afterwards we extract the result for Serine which is the real mutation in this case. SNAP shows as result that the exchange of Asparagine to Serine at this position is neutral with a very high accuracy. This means that this certain mutation at this position cause very likely no structural and functional changes of the protein.

Substitution Prediction Reliability Index Expected Accuracy
S Neutral 8 96%

A detailed list of all possible substitutions can be found [here]


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SIFT Prediction

Next, we used SIFT Prediction which displays if a mutation is neutral or not. Therefore, it first shows a row which contains a score for the particular mutation position of a certain amino acid. The amino acid which are not tolerated at this position are colored red. Besides, it also constructs a table which lists the amino acids that are predicted as tolerated and not-tolerated.

In this case, the only substitution that is not tolerated is the one to Tryptophan. The substitution to Serine is tolerated and makes no problems. This means that this mutation at this position is probably neutral and will not cause any structural and function changes of the protein.


SIFT Matrix:
Each entry contains the score at a particular position (row) for an amino acid substitution (column). Substitutions predicted to be intolerant are highlighted in red.

Sift legend.png
Figure 5: Sift Table

SIFT Table
Threshold for intolerance is 0.05.
Amino acid color code: nonpolar, uncharged polar, basic, acidic.
Capital letters indicate amino acids appearing in the alignment, lower case letters result from prediction.



Predict Not ToleratedPositionSeq RepPredict Tolerated
w29N0.78cfMHiYpVgLRNdQTASKE




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PolyPhen2 Prediction

Finally, we also regarded the PolyPhen2 prediction for this mutation. This prediction visualize how strongly damaging the mutation probably will be. Therefore it gives the result for two possible cases: HumDiv and HumVar. HumDiv is the preferred model for evolutionary rare alleles, dense mapping of regions identified by genome-wide association studies and analysis of neutral selection. In contrast, HumVar is the preferred model for diagnostic of Mendelian diseases which require distinguishing mutations with drastic effects from all remaining human variations including abundant mildly deleterious alleles. We decided to look at both possible models, which agree in the most cases.

In this case both models predict that the mutation is benign (Picture 6 and Picture 7). This means that the mutation is neutral and will probably not damage the structure and the function of the protein.

Picture 6: HumDiv prediction
Picture 7: HumVar prediction


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