Difference between revisions of "Sequence-based mutation analysis of ARSA"

From Bioinformatikpedia
(→‎Lifting the curtain - Our predictions vs. HGMD and dbSNP)
(→‎Mutation 9)
Line 736: Line 736:
 
|}
  
|}
 
|}
  
|}
−
Cystein and Glycine are both hydrophilic amino acids. One difference is the size: Gly is the smallest of the amino acids while Cys is a little bigger. But more important Cystein contains sulfur which is important for building sulfur bridges. So function should be changed by this mutation.
 +
Cystein and Glycine are both hydrophilic amino acids. One difference is the ize. Gly is the smallest of the amino acids, while Cys is a little bigger. But even more important Cystein contains sulfur which is important for building sulfur bridges. Sulfur birdges serve as important covalent interactions for a lot of protein structures. Thus, if a Cysteine, which forms sulfur bridges, is replaced by another amino acid, the structure migth be dramatically changed and this again is likely to affect the function. This is also reflected by the scores in the substitution matrices. Furthermore, the residue is conserved across homologs to ARSA and all prediction methods agree, that this mutation is non-neutral. <br>
  +
It does not disrupt any secondary structure elements, but all other facts indicate, that this mutation is has a non-neutral effect on the function.
−
The conservation of Cystein is very high in the MSA and the scores in the substitution matrices are very low. Also, all 4 methods agree that this mutation changes the function of the Arylsulfatase A.
  
−
This mutation causes Metachromatic leukodystrophy.
  
 
|-
  
|-
 
|}
  
|}
−  
−  
   
 
==== Mutation 10 ====
  
==== Mutation 10 ====

Revision as of 17:01, 12 August 2011

Introduction

Many mutations in the human genome are suspected to have an impact on protein function. Thus, the prediction of the effects of these mutations on the function - especially for disease causing mutation - is a very important task. In this TASK, we will apply different sequence based methods to predict mutation effects on the protein's function and then try to discriminate neutral from non-neutral mutations.
We randomly picked 10 missense mutations from dbSNP and HGMD. At this point, we act like we did not know which of these mutations is causing the disease and which is not. After having applied the methods and interpreted the results, we are going to lift the curtain and check if our guesses were correct. The mutations, we picked are summarized in the table below:

Nr. mutation position
1 Asp-Asn 29
2 Pro - Ala 136
3 Gln-His 153
4 Trp-Cys 193
5 Thr-Met 274
6 Phe -Val 356
7 Thr-Ile 409
8 Asn-Ser 440
9 Cys-Gly 489
10 Arg-His 496

Substitution Matrices

A first very rough guess on the effect of mutation can be made by looking at the standard substitution matrices, like the BLOSUM and PAM matrices. Low scores in these matrices indicate, that mutations of two amino acids are rarely observed and thus the amino acids should have very different physico-chemical properities. Consequently substitution with low scores might affect structure and/or the function of the protein.
Substitutions with a high score are observed very frequently. Thus the properties of the amino acids are similar and thus the substiotion is not very likely to affect the protein's structure or function.
When doing this analysis, we have to keep in mind, that this is a very inaccurate method to "predict" the impact of a certain mutation, as these matrices are calculated with a lot of proteins, which evens out effects specific to our protein, protein familiy respectively. But it can give a first gues, if the mutations is likely to occur in general or not.
We extracted the scores for our mutations from BLOSUM62, PAM1 and PAM100 and summarized these in the following table. Additionaly, we extracted the lowest score possible for any substitution of the amino acid of interest.


Nr. Substitution BLOSUM62 PAM1 PAM250
1 Asp(D) -> Asn(N) 1 (worst: -4) 36 (worst: 0) 7 (worst: 0)
2 Pro(P) -> Ala(A) -1 (worst: -4) 22 (worst: 0) 11 (worst: 0)
3 Gln(Q) -> His(H) 0 (worst: -3) 20 (worst: 0) 7 (worst: 0)
4 Trp(W) -> Cys(C) -2 (worst: -4) 0 (worst: 0) 1 (worst: 1)
5 Thr((T) -> Met(M) -1 (worst: -3) 2 (worst: 0) 1 (worst: 0)
6 Phe(F) -> Val(V) -1 (worst: -4) 1 (worst: 0) 10 (worst: 1)
7 Thr(T) -> Ile(I) -2 (worst: -3) 7 (worst: 0) 4 (worst: 0)
8 Asn(N) -> Ser(S) 1 (worst: -4) 34 (worst: 0) 8 (worst: 0)
9 Cys(C) -> Gly(G) -3 (worst: -4) 1 (worst: 0) 4 (worst: 0)
10 Arg(R) -> His(H) 0 (worst: -3) 8 (worst: 0) 5 (worst: 1)

PSI-BLAST

An improvement to looking at the standard substitution matrices from above could be made by generating a substitution matrix, which is specific to our protein and its homologs. Such a matrix can be obtained by executing a PSI-BLAST search. To infer the position specific sequence profile, we executed PSI-BLAST with the following command: 


blastpgp -i ARSA.fasta -d /data/blast/nr/nr -e 10E-6 -j 5 -Q psiblast.mat -o psiblast_eval10E_6.it.5.new.txt

The graphic shows the relevant lines of the profile matrix regarding our mutated positions. The scores of interest - which score our mutation substitutions - are highlighted in green. 


Last position-specific scoring matrix computed, weighted observed percentages rounded down, information per position, and relative weight of gapless real matches to pseudocounts
          A  R  N  D  C  Q  E  G  H  I  L  K  M  F  P  S  T  W  Y  V   A   R   N   D   C   Q   E   G   H   I   L   K   M   F   P   S   T   W   Y   V
  29 D   -5 -5 -2  8 -7 -3 -1 -4 -4 -6 -7 -4 -6 -7 -5 -3 -4 -7 -6 -6    0   0   0 100   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  2.49 1.56
 153 Q    3  2 -1  4 -4 -1 -1 -2  0 -2 -3 -3  4 -2 -3 -1 -2 -3 -2 -2   26  10   3  23   0   3   3   3   2   2   1   1  13   2   1   3   2   0   1   2  0.53 1.48
 274 T   -3 -4 -3 -4 -2 -4 -4 -5 -5 -4 -4 -4 -3 -5 -4  1  8 -6 -5 -3    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   7  92   0   0   0  1.94 1.62
 409 T   -1  0  0 -1 -2 -1 -1  0 -1 -1 -1  0 -1 -1  3  0  1  6  0 -1    5   5   5   4   1   3   4   8   1   3   6   5   1   2  13   6   8  11   3   4  0.26 0.95
 489 C    2 -1  1 -4  8 -4 -4 -2 -1 -1 -2 -3 -1 -4 -4  0  0  5 -1 -3   15   4   8   0  36   0   0   2   1   3   3   1   1   0   0   6   5   9   2   0  0.99 1.22
 440 N   -5 -3  6  5 -6 -2 -1 -4 -3 -6 -6 -3 -6 -6  2 -2 -3 -6 -6 -5    0   1  46  36   0   1   2   0   0   0   0   1   0   0  10   1   1   0   0   0  1.48 1.67
 356 F   -3 -1 -5 -5 -3  0 -1 -6  1  3  0 -1  0  2 -6 -3 -2 -3  5  3    1   4   0   0   1   5   4   0   3  18   8   5   2   8   0   1   2   0  20  20  0.59 1.62
 193 W   -2  4  2  3 -5  0  0 -2  0 -3 -4  1 -3 -1 -2 -1 -2  1  1 -3    3  25  11  16   0   4   5   3   2   2   1   7   0   2   2   4   2   2   5   2  0.46 1.45
 136 P   -3 -5 -5 -5 -6 -4 -4 -5 -5 -6 -6 -4 -6 -7  9 -4 -4 -7 -6 -5    1   0   0   0   0   0   0   0   0   0   0   0   0   0  98   0   0   0   0   0  3.03 1.61
 496 R   -3  1  0 -3 -4  1  1 -1  1 -3  1  1 -2  2  4  0 -3 -1 -1 -3    1   7   4   1   0   5  10   4   3   1  16   9   0   9  20   8   1   1   1   1  0.34 0.96

Multiple sequence alignments

Another interesting feature one could look at is the conservation of the wild type and mutant residues of our protein in the sequence of homologs. To calculate this, we first downloaded the HSSP file for ARSA to get all proteins, which are homologuous to it. Then we downloaded all mammalian protein sequences from Uniprot. This was achieved by searching for the term taxonomy:40674, which codes for all mammalian protein sequences. We saved all sequences in one multiple fasta file. Then we extracted all homologuous mammalian proteins to human ARSA by mapping the ids from the HSSP file to sequence ids in the multi fasta file. This yielded 75 homologuous mammalian sequences to human ARSA.
Next, we calculated a multiple sequence alignments of these proteins (including ARSA) with Muscle. The Jalview image of the alignment is shown below.

Multiple sequence alignments of all 75 homologuous sequences using muscle

The following table shows the conservation of the original amino acid in the reference sequence and their mutations at the respective positions.

pos conservation - reference conservation - mutant
29 0.86 0
153 0.14 0
274 0.87 0
409 0.35 0.16
489 0.80 0.05
193 0.13 0
356 0.15 0
440 0.15 0
496 0.14 0.01
136 0.93 0

Secondary Structure

Secondary structure is an important structural feature of the protein, which also stabilizes the overall tertiary structure and is therefore also important for a proper functioning of the protein. Mutations, which are located within secondary structure elements might destroy the secondary structure and migth therefore have an impact on the protein function. To consider the position of the mutations, relative to the secondary structure of ARSA, we generated the following map:

Sec Struct Mutations ARSA.png

As one can see in the picture above, none of the mutations is in the middle of a secondary structure element. Only the mutations 1,2,4 and 5 are close to or - depending on the prediction method - at the border of secondary structure elements.

Prediction of effect

SNAP

SNAP uses a neural-network approach to predict effects of single amino acid substitutions on protein function. It uses in silico derived protein information - like secondary structure, conservation, solvent accessibility, etc. - for the prediction. <ref> SNAP: predict effect of non-synonymous polymorphisms on function. Yana Bromberg and Burkhard Rost Nucleic Acids Research, 2007, Vol. 35, No. 11 3823-3835 </ref>
We ran snap using the following command: 


snapfun -i ARSA.fasta -m mutants.txt -o snap.out

output: 


nsSNP	Prediction	Reliability Index	Expected Accuracy
-----	------------	-------------------	-------------------
D29N	Non-neutral		7			96%
Q153H	 Neutral 		0			53%
T274M	Non-neutral		6			93%
T409I	Non-neutral		1			63%
C489G	Non-neutral		5			87%
W193C	Non-neutral		3			78%
F356V	 Neutral 		1			60%
N440S	Non-neutral		2			70%
R496H	 Neutral 		1			60%
P136A	Non-neutral		4			82%

SNAP predicts three of our proteins to be neutral, the other non-neutral. In order to analyze all possible combinations of amino acid substitutions from the above mutated positions, we used the Generate Mutants tool on http://rostlab.org/services/snap/submit to create all possible exchanges from the following pattern: referenceAminoAcidPosition* . Then we again executed snap: 


snapfun -i ARSA.fasta -m all_mutants.txt -o snap_all.out

Next, we wrote a perl script to parse and summarize the SNAP output in the following table, which shows which amino acid substitutions are Non-neutral or Neutral. We consider a residue as important if 66-100 % of all possible substitutions are Non-Neutral, as probably important if 33-66 % of possible substitutions are Non-Neutral and as not important, if 0-33 % of all possible substitutions are Non-Neutral.

ref\mutation important A R N D C Q E G H I L K M F P S T W Y V
D29 yes Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
Q153 yes Non-neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
T274 yes Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
T409 yes Neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
C489 yes Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
W193 yes Non-neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
F356 probably Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Neutral Non-neutral Non-neutral Neutral Neutral Neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Neutral Neutral
N440 yes Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral
R496 yes Non-neutral Non-neutral Non-neutral Non-neutral Neutral Non-neutral Non-neutral Neutral Non-neutral Non-neutral Neutral Non-neutral Neutral Non-neutral Non-neutral Non-neutral Non-neutral Neutral Non-neutral
P136 yes Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral Non-neutral

SIFT

SIFT predicts the effect of amino acid substitutions by building a multiple alignment and then calculating the probability of each possible substitution. The score in the SIFT-output is the probability of the substitution. SIFT predicts a substitution as damaging if this probability is <= 0.05 and as tolerated if the probability is > 0.05. The median conservation in the output measures the diversity of the sequences used in the multiple alignment. It should be between 2.75 and 3.25. Higher values indicate that the sequences were too closely related. <ref>http://sift.jcvi.org/www/SIFT_help.html</ref> We used SIFT with the UniProt-TrEMBL 2009 Database and uploaded a file containing our chosen mutations: 

D29N
P136A
Q153H
W193C
T274M
F356V
T409I
N440S
C489G
R496H

As median conservation we used the standard parameter 3.00 and we excluded all sequences with a sequence identity higher than 90%.

Mutation NR Substitution predicted score median conservation comment
1 D29N AFFECT PROTEIN FUNCTION 0.00 3.04
2 P136A AFFECT PROTEIN FUNCTION 0.00 3.07
3 Q153H TOLERATED 0.29 3.04
4 W193C AFFECT PROTEIN FUNCTION 0.04 3.04
5 T274M AFFECT PROTEIN FUNCTION 0.00 3.04
6 F356V TOLERATED 0.81 3.04
7 T409I AFFECT PROTEIN FUNCTION 0.02 3.48 low confidence
8 N440S TOLERATED 0.07 3.08
9 C489G AFFECT PROTEIN FUNCTION 0.00 3.56 low confidence
10 R496H TOLERATED 0.28 3.56

PolyPhen

PolyPhen predicts wether a mutation is damaging or not by using a Naïve-Bayes-approach. The score is the posterior probability that the mutation is damaging.<ref>http://genetics.bwh.harvard.edu/pph2/dokuwiki/overview</ref> We used PolyPhen with standard parameters. The results are shown below.

Mutation NR Substitution HumDiv HumVar Link (expires in September)
predicted score predicted score
1 D29N probably damaging 1.000 probably damaging 0.999 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479962.html
2 P136A probably damaging 1.000 probably damging 0.999 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479963.html
3 Q153H possibly damaging 0.945 possibly damaging 0.520 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479964.html
4 W193C probably damaging 0.977 possibly damaging 0.633 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479965.html
5 T274M probably damaging 1.000 probably damaging 1.000 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479966.html
6 F356V benign 0.000 benign 0.001 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479967.html
7 T409I probably damaging 0.961 benign 0.432 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479968.html
8 N440S possibly damaging 0.834 benign 0.255 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479969.html
9 C489G damaging 0.999 probably damaging 0.906 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479970.html
10 R496H benign 0.003 benign 0.000 http://genetics.bwh.harvard.edu/ggi/pph2/6b8e887bab2c4971aff12f9579630878eaaed666/479971.html

Summary of the prediction results

To compare the results of the different prediction methods we created the table below. If a mutation was predicted to have an effect, a "X" was set, if a mutation was predicted to have no effect, a "-" was set. For PolyPhen "X" means "damaging" or "probably damaging", a "/" means "possibly damaging" and a "-" means "benign".

Mutation NR Substitution SNAP SIFT PolyPhen
HumDiv HumVar
1 D29N X X X X
2 P136A X X X X
3 Q153H - - / /
4 W193C X X X /
5 T274M X X X X
6 F356V - - - -
7 T409I X X X -
8 N440S X - / -
9 C489G X X X X
10 R496H - - - -

Summary and Discussion

In this section we compare the results of the previous analyses and additionaly use pymol mutagenesis images and the physico-chemical properties of the amino acids to make our final guess of the impact of the mutation. All mutations are listed below, together with a pymol mutagenesis image and a description of the properties of the mutations. We also included short summary tables of the methods we applied and added a short discussion/interpretation of the results. For a detailed descitption of the summary tables, please read the individual sections.


Mutation 1

Nr. mutation position reference mutation both
1 Asp-Asn 29
29 arsa asp.png
29 arsa asn.png
29 arsa both.png
Description of Asp-Asn
Aspartic acid (Asp)
Asparagine (Asn)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
1 (worst: -4) 36 (worst: 0) 7 (worst: 0) -5 X X X X important 0.86 yes (at the border)

Aspartic acid is an acidic amino acid while Asparagine is a hydrophilic amino acid. So the mutation changes the behaviour towards water as well as to the pH. The lysosomal enzyme ARSA is active at a very low pH value, thus acidic amino acids are preferred in this environment. Consequently the effect could be deleterious. This hypothesis is supported by all predictions and also the substitution matrices show rather low values. The mutation is located at the border of a beta sheet, which is also an indicator for a possible deleterious effect. Also the conservation of the amino acid is very high in the MSA of related sequences, which indicates, that the residue is quite important. Furthermore it is classified as important residue by our SNAP analysis of all possible mutants, i.e. most of the substitutions lead to a deleterious effect.
If there is an effect, it is not introduced by a structural change of the aminpo acid itself - structures are very similar (see mutagenesis images abbove) - but through the drastic change of the amino acid property. Regarding to our analysis we classify this mutation as deleterious.


Mutation 2

Nr. mutation position reference mutation both
2 Pro - Ala 136
136 arsa PRO.png
136 arsa ALA.png
136 arsa both.png
Description of Pro-Ala
Proline (Pro)
Alanine (Ala)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-1 (worst: -4) 22 (worst: 0) 11 (worst: 0) -3 X X X X important 0.93 yes (at the border)

Proline and Alanine are both hydrophobic amino acids. In contrast to mutation 1, the behaviour towards water does not change. As Proline is a cyclic amino acid, it can "break" alpha-helices and is structurally very important. It is even located at the border of an alpha-helix. Thus, the change to the small amino acid Alanine could introduce a big structural change, despite the similarity, regarding to their chemical properties. This structural change might e.g. occur due to an extension of the alpha-helix.
For this mutation, all predictions yield damaging effects and the substitution matrices indicate, that a substitution from Pro to Ala is very unlikely. Again, the conservation of the amino acid is very high in the MSA of related sequences, which indicates, that the residue is quite important.
Furthermore it is classified as important residue by our analysis of all possible mutants, i.e. most of the substitutions lead to a deleterious effect.
Due to all these indicators, we guess that this amino acid is deleterious and leads t oan outbreak of the disease.


Mutation 3

Nr. mutation position reference mutation both
3 Gln-His 153
153 arsa GLN.png
153 arsa HIS.png
153 arsa both.png
Description of Gln-His
Glutamine (Gln)
Histidine (His)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
0 (worst: -3) 20 (worst: 0) 7 (worst: 0) 0 - / / - important 0.14 no

Glutamine is a hydrophilic amino acid while Histidine is a basic amino acid. So the behaviour towards water changes as well as the charge of the amino acid. Furthermore, Glutamine and Histidine are very different in structure. Histidine is bigger and needs much more space than Glutamine, which could have an influence on the structure of ARSA (see above pymol images).
In this case the mutation is not located within a secondary structure element and it is also not conserved in the MSA. Further on, the values in PAM and the PSSM are quite high. The value in the BLSOUM62 matrix however lies in the mid range. These factors indicate, that the mutation should not have a severe effect.
The predictions made by SNAP, SIFT and Polyphen are not fully consistent. Whereas SIFT and SNAP predict a neutral effect, Polyphen predicts a benign effect. Regarding to the above results, we tend to classify this mutation as neutral. However this prediction is not supported by a striking evidence.


Mutation 4

Nr. mutation position reference mutation both
4 Trp-Cys 193
193 arsa TRP.png
193 arsa CYS.png
193 arsa both.png
Description of Trp-Cys
Tryptophan (Trp)
Cysteine (Cys)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-2 (worst: -4) 0 (worst: 0) 1 (worst: 1) -5 X X / X important 0.13 no

Tryptophan is a hydrophobic, aromatic amino acid while Cysteine is a hydrophilic amino acid. So the behaviour towards water changes dramatically. Also, Trp is the largest amino acid while Cys is a rather small amino acid. So the space needed for the amino acid changes also. Structural features and chemical properties indicate an influence on the structure and function.
The wild type residue is not conserved across the homologs of ARSA, which could mean that it is a not very important residue. The mutation is not located within a secondary structure element. This could indicate a neutral substitution. However, all substitution matrices yield very low values for the given substitution, thus the substitution is very unlikely. The predictions of SNAP, SIFT and Polyphen suggest a damaging effect. Also our SNAP "all combination" analysis assigns importance to the residues. Like for mutation 3 the results here are again a bit contradictory, but because of the great evidence of the prediction tools and the low values for the scoring matrices, assign a deleterious effect to this mutation.


Mutation 5

Nr. mutation position reference mutation both
5 Thr-Met 274
274 arsa THR.png
274 arsa MET.png
274 arsa both.png
Description of Thr-Met
Threonine (Thr)
Methionine (Met)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-1 (worst: -3) 2 (worst: 0) 1 (worst: 0) -3 X X X X important 0.87 yes (at the border)

Threonine is a hydrophilic amino acid while Methionine is a hydrophobic amino acid. So the behaviour towards water changes. AMethionine has a very long sidechain while Threonine does not. So the physico-chemical features indicate, that the structure of ARSA could be altered by this mutation.
Besides these properties, the mutation is located within a secondary structure element, the residue (Thr) is highly conserved across homologs and all prediction tools predict a deleterious effect on the enzyme's function. Further on, the values in the substitution matrices are very low, indicating a deleterious effect. This time everything indicates a deleterious effect, thus predict this mutation to be harmful.


Mutation 6

Nr. mutation position reference mutation both
6 Phe -Val 356
356 arsa PHE.png
356 arsa VAL.png
356 arsa both.png
Description of Phe-Val
Phenylalanine (Phe)
Valine (Val)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-1 (worst: -4) 1 (worst: 0) 10 (worst: 1) 3 - - - - probably important 0.15 no

Phenylalanine and Valine are both hydrophobic amino acids. So the only impact on structure could come frome the structural differences between Phe and Val. Phe has a aromatic ring and due to that needs more space than Val.
When looking at the substitution-matrices, one can notice that the scores are not very high but also not really low. The prediction methods all agree, that this mutation should have no harmful effect. Furthemore, the conservation in the MSA is very low and the mutation is not disrupting a secondary structure element, which are indicators that the mutation should not have a deleterious effect.
In this case, we guess that this mutation should be neutral.


Mutation 7

Nr. mutation position reference mutation both
7 Thr-Ile 409
409 arsa THR.png
409 arsa ILE.png
409 arsa both.png
Description of Thr-Ile
Threonine (Thr)
Isoleucine (Ile)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-2 (worst: -3) 7 (worst: 0) 4 (worst: 0) -1 X X - X important 0.35 no

Threonine is a hydrophilic amino acid while Isoleucine is a hydrophobic amino acid. So the behaviour towards water changes. Furthermore they are structurally not very similar (see mutagenesis image).
All prediction methods except the HumVar-Mode of PolyPhen assign a functional change to this mutation, which is also a clear indicator for a non-neutral effect. Our SNAP analysis also classifies this position to be important.
The conservation in the MSA is not high and the mutation does not disrupt a secondary structure element, which is again an indicator that a mutation at this position might not cause any effect. However, the scores in the substitution matrices are rather low, which supports the prediction tools.
Due to the stronger evidence for a non-neutral effect, we expect this mutation to be non-neutral.

Mutation 8

Nr. mutation position reference mutation both
8 Asn-Ser 440
440 arsa ASN.png
440 arsa SER.png
440 arsa both.png
Description of Asn-Ser
Asparagine (Asn)
Serine (Ser)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
1 (worst: -4) 34 (worst: 0) 8 (worst: 0) -2 X / - - important 0.15 no

Asparagine and Serine are both hydrophilic amino acids. Also they are of similar size. The scores in the substitution matrices for this mutation are very high and the conservation in the MSA is very low, which indicates that the residue is not very important to the protein. Furthermore, the mutation is not disrupting a secondary structure element, which also favors the neutral effect hypothesis. The prediction methods do not agree on the effect of the mutation. SNAP predicts a non-neutral effect, whereas SIFT and Polyphen(HumVar) predict a neutral effetc. Polyphen(Humdiv) predicts a benign effect. However, the indications for a neutral effect predominate, so we guess, that this mutation has a neutral effect.

Mutation 9

Nr. mutation position reference mutation both
9 Cys-Gly 489
489 arsa CYS.png
489 arsa GLY.png
489 arsa both.png
Description of Cys-Gly
Cystein (Cys)
Glycine (Gly)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen (HumDiv) Polyphen (HumVar) SIFT Residue Importance Conservation in MSA Disrupting SS
-3 (worst: -4) 1 (worst: 0) 4 (worst: 0) -2 X X X X important 0.80 no

Cystein and Glycine are both hydrophilic amino acids. One difference is the ize. Gly is the smallest of the amino acids, while Cys is a little bigger. But even more important Cystein contains sulfur which is important for building sulfur bridges. Sulfur birdges serve as important covalent interactions for a lot of protein structures. Thus, if a Cysteine, which forms sulfur bridges, is replaced by another amino acid, the structure migth be dramatically changed and this again is likely to affect the function. This is also reflected by the scores in the substitution matrices. Furthermore, the residue is conserved across homologs to ARSA and all prediction methods agree, that this mutation is non-neutral.
It does not disrupt any secondary structure elements, but all other facts indicate, that this mutation is has a non-neutral effect on the function.

Mutation 10

Nr. mutation position reference mutation both
10 Arg-His 496
496 arsa ARG.png
496 arsa HIS.png
496 arsa both.png
Description of Arg-His
Arginine (Arg)
Histidine (His)
BLOSUM62 PAM1 PAM250 PSI-BLAST SNAP Polyphen SIFT (HumDiv) Polyphen (HumVar) Residue Importance Conservation in MSA Disrupting SS
0 (worst: -3) 8 (worst: 0) 5 (worst: 1) 1 - - - - important 0.14 no

Arginine and Histidine are both basic amino acids so the only effect could come from the difference in size of the two. The conservation of Arginine in the MSA is very low and all 4 methods agree in the fact that this mutation is not disease-causing. Also the fact that the mutation does not disrupt a secondary structure element supports this idea. The mutation is classified as SNP and due to that not disease-causing.

Lifting the curtain - Our predictions vs. HGMD and dbSNP

1: This is supported by the Uniprot annotation, which associates it to infantile-onset Metachromatic leukodystrophy. It causes a severe reduction of enzyme activity.
2: Regarding to HGMD, this mutation leads to the outbreak of the disease.
3: This is however not the case. HGMD states, that the mutation is associated to Metachromatic Leukodystrophy.
4: However, HGMD does not contain this mutation and dbSNP does not assign a deleterious effect. The mutation is a single nucleotide polymorphism (SNP), which - by defintion - occurs in a certain part of the population. As Metachromatic leukodystrophy is not very widespread this mutation should be a non-damaging natural variant.
5: HGMD assigns a deleterious effect to the mutation.
6: dbSNP classifies this mutation as SNP, so it should not be harmful.
7: The mutation is known to cause Metachromatic Leukodystrophy.
8: DbSNP classifies this mutation as SNP, so it should not be harmful. So the mutation should not have a very dramatic effect.

References

<references />

Retrieved from "https://i12r-studfilesrv.informatik.tu-muenchen.de/wiki/index.php?title=Sequence-based_mutation_analysis_of_ARSA&oldid=11013"