From Bioinformatikpedia
Revision as of 15:18, 26 June 2011 by Uskat (talk | contribs) (PSSM analysis)

General Information

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

Pysicochemical Properities

First of all, we explored the amino acid properties and compared them for the original and the mutated amino acid. Therefore we created the possible effect that 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.

Visualisation of the Mutation

In the next step, we created the visualization of the muation with PyMol. Therefore we created a picture for the original amino acid, for the new mutated amino acid and finally for both together in one picture whereas the mutation is white colored. 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 extremly. Therefore, this exchange will probably not cause huge effects on the protein structure and function.

picture original aa picture mutated aa combined picture
Amino acid Asparagine
Amino acid Serine
Picture which visualize the mutation

Subsitution 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 accoding amino acid substitution, the most frequent value for the substitution of the examined amino acid and the rarest substitution.

In this case, the substitution of Asparagine to Serine has very high values that is nearer to the values for the most frequent subsitution for PAM1. Contrary for PAM250 the value for the amino acid subsitution Leucine to Arginine is average. This means the most frequent subsitution value is almost as far as the rarerest subsitution from the the underlying value. The difference between the two PAMs can be ascribed to the different preparations of these two kind of substitutions matrices. For the PAM1-matrice the substitution rate is 1% which means the prabability 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. All in all, 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 drastical 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)

PSSM analysis

Besides, we looked additional at the position specific scoring matrix (PSSM) for ouer 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 an BLOSOUM. We extracted the self-information and the expceted self-information from the PSSM....

self-information expexted self-information
Asn 1 5
Ser 3 24

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 on this position. The regarded mutation is presented by the first colored column. Here we can see, that the most other mammalians have on this Position another amino acid. Only one other mammalian agrees and has at this position an Asparagine. Therefore, the mutation on this position is bad conserved and has probably no structural and functional change as a result.

Mutation in the multiple alignment

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 drastical the mutation can be. In this case both tools agree and predict at the position of the mutation the end of a sheet. This has a result, 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 have as a result structural change of the protein which can cause a functional loose for the protein.


Comparison with the real Structure:

Afterwards we also visualize the position of the muation (red) in the real 3D-structure of PDB 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 mutationposition agree 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 what could has as a result that the structural change is not as drastical. Otherwise it can cause a change of the further secondary structure element which can has a functional loose as a consequence. We think that a structural change is mor probable, because when we look at the prediction the mutation is not the complet last sheet element. Therefore, it cause some structural changes in the sheet which will have probably structural changes of the protein as a consequence which can affect the protein function.

Mutation at position 29
Mutation at position 29 - detailed view

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 has a result that the exhange from Asparagine to Serine at this position is neutral with a very high accuracy. This means that this certain mutation on 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]

SIFT Prediction

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
29 sift.png.png

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

PolyPhen2 Prediction

Finally, we also regarded the PolyPhen2 prediction for this muation. This prediction visualizes have strongly demaging the mutation probably will be. Therefore it gives the result for two possible cases: HumDiv and HumVar. HumDiv is a prefered model for evaluation rare allels, dense mapping of regions identified by genome-wide assiociation studies and analysis of neutral selection. In contrast, HumVar is a prefered model for diagnostic of Mendelian diseases which require distinguishing mutations with drastic effects from all remaining human variations including abundant mildly deleterious allels. We decided to look at both possible models, which are agrees in the most cases.

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

HumDiv prediction
HumVar prediction