Sequence-based mutation analysis TSD
There was only one catch and that was Catch-22, which specified that a concern for one's own safety in the face of dangers that were real and immediate was the process of a rational mind. Orr was crazy and could be grounded. All he had to do was ask; and as soon as he did, he would no longer be crazy and would have to fly more missions. Orr would be crazy to fly more missions and sane if he didn't, but if he was sane, he had to fly them. If he flew them, he was crazy and didn't have to; but if he didn't want to, he was sane and had to. Yossarian was moved very deeply by the absolute simplicity of this clause of Catch-22 and let out a respectful whistle.
"That's some catch, that Catch-22," he observed.
"It's the best there is," Doc Daneeka agreed.
-Catch 22
The journal for this task can be found here.
Contents
Mutations
Dataset
<figure id="fig:snpsOnstr">
</figure>
The following SNPs, selected by an unbiased source, will be analysed: M1V, L39R, C58Y, L127R, R170W, R178H, S210F, D258H, L451V and E482K.
Chemical properties
The biochemical properties of the wildtype and mutant amino acids of the chosen SNPs ale listed in <xr id="tab:biochem"/>. Displayed are the hydrophobicity in form of the hydropathy index (Kyte Doolittle) and the according category, the volume with the matching characterisation, the charge and the grantham score. All are taken from <ref name="snpdbe">http://www.rostlab.org/services/snpdbe/</ref>.
The Grantham scores predicts the effect of substitutions between amino acids based on chemical properties, including polarity and molecular volume. It categorizes codon replacements into classes of increasing chemical dissimilarity, and it ranges from 5 to 215<ref name="grantham">Grantham R. Amino acid difference formula to help explain protein evolution. Science 1974; 185: 862-864 </ref>.
The highest Grantham score has the SNP C58Y where both amino acids have a high difference in volume. The change from glutamic acid to lysine on position 482 seems very severy as the charge changes from negative to positive. Altogether in 6 out of 10 cases the charge is different in wildtype and mutant. In 4 mutations the hydrohpobicity is changed from polar to non-polar or the other way round.
TODO
<figtable id="tab:biochem">
| Mutation | Wildtype | Mutant | Grantham score | ||||
|---|---|---|---|---|---|---|---|
| Hydrophpbicity | Volume | Charge | Hydrophpbicity | Volume | Charge | ||
| M1V | 1.9 (nonpolar) | 162.9 (bulky) | neutral | 4.2 (nonpolar) | 140.0 (small) | neutral | 21 |
| L39R | 3.8 (nonpolar) | 166.7 (bulky) | neutral | -4.5 (polar) | 173.4 (bulky) | positive | 102 |
| C58Y | 2.5 (polar) | 108.5 (small) | neutral | -1.3 (polar) | 193.6 (bulky) | neutral | 194 |
| L127R | 3.8 (nonpolar) | 166.7 (bulky) | neutral | -4.5 (polar) | 173.4 (bulky) | positive | 102 |
| R170W | -4.5 (polar) | 173.4 (bulky) | positive | -0.9 (nonpolar) | 227.8 (bulky) | neutral | 101 |
| R178H | -4.5 (polar) | 173.4 (bulky) | positive | -3.2 (polar) | 153.2 (bulky) | neutral | 29 |
| S210F | -0.8 (polar) | 89.0 (tiny) | neutral | 2.8 (nonpolar) | 189.9 (bulky) | neutral | 155 |
| D258H | -3.5 (polar) | 111.1 (small) | negative | -3.2 (polar) | 153.2 (bulky) | neutral | 81 |
| L451V | 3.8 (nonpolar) | 166.7 (bulky) | neutral | 4.2 (nonpolar) | 140.0 (small) | neutral | 32 |
| E482K | -3.5 (polar) | 138.4 (bulky) | negative | -3.9 (polar) | 168.6 (bulky) | positive | 56 |
</figtable>
Structural observations
In <xr id="tbl:pymolstruc"/> all SNPs are displayed with the wildtype and mutant structure. The M1V mutation could not be displayed as it is nor part of the pdb reference structure. Especially the mutations R170W R178H D258H seem disrupting as they destroy the H-bonds present in the wildtype. E482K, L451V and L39R express comparably weak alterations of the natural structure. There are 5 ring-structure substitutions which could be an indicator of a more drastic structure change leading to functional implications.
<figtable id="tbl:pymolstruc">
</figtable>
Using your secondary structure predictions from the previous tasks, investigate whether the mutations are inside secondary structure elements (Helix, Strand) or not.
<figtable id="tab:pssm_msa">
| Mutation | DSSP structure | PsiPred wt | PsiPred mt |
|---|---|---|---|
| M1V | N/A | ||
| L39R | E | ||
| C58Y | - | ||
| L127R | E | ||
| R170W | E | ||
| R178H | S | ||
| S210F | B | ||
| D258H | - | ||
| L451V | T | ||
| E482K | H |
</figtable>
Substitution matrices
BLOSUM62 and PAM
<xr id="tab:pamnlos"/> shows the substitution scores of all mutations for the BLOSUM62, PAM1 and PAM250 matrix. PAM1 and PAM250 are the extremes for this type of substitution matrix and might not be the most commonly applied ones. BLOSUM62 however is one of the standard matrices used by default in large tools like BLAST. The BLOSUM62 matrix used here is the incorrectly calculated version, since it generally preforms better <ref name="wrongblosum">Styczynski, M. P., Jensen, K. L., Rigoutsos, I., & Stephanopoulos, G. (2008). BLOSUM62 miscalculations improve search performance. Nature biotechnology, 26(3), 274–275. Nature Publishing Group. Retrieved from http://www.nature.com/nbt/journal/v26/n3/full/nbt0308-274.html</ref>.
An entry in the PAM matrix describes the probability that the wildtype amino acid is replaced by the mutant amino acid in a given timeframe, e.g. 1PAM or 250PAM. Entries in a BLOSUM matrix are slightly less intuitive and describe the ratio between the wildtype and mutant appearing in a biologically founded sequence or them appearing by chance. In addition the values are usually displayed as log-odds. Refer to the original publication <ref name="BLOSUM">Henikoff,S. and Henikoff,J.G. (1992) Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences of the United States of America, 89, 10915-9.</ref> for details.
<figtable id="tab:pamnlos">
| Mutation | BLOSUM62 <ref name="blosumvalsrc">http://www.ncbi.nlm.nih.gov/Class/BLAST/BLOSUM62.txt</ref> | PAM (1<ref name="pamvalsrc1">http://www.icp.ucl.ac.be/~opperd/private/pam1.html</ref>/250<ref name="pamvalsrc250">http://www.icp.ucl.ac.be/~opperd/private/pam250.html</ref>) |
|---|---|---|
| M1V | 1 | 17/400 |
| L39R | -2 | 1/200 |
| C58Y | -2 | 3/300 |
| L127R | -2 | 1/200 |
| R170W | -3 | 2/200 |
| R178H | 0 | 8/500 |
| S210F | -2 | 2/200 |
| D258H | -1 | 3/400 |
| L451V | 1 | 11/1500 |
| E482K | 1 | 7/800 |
</figtable>
In a BLOSUM62 matrix, the values range between -4, a very unlikely substitution to 11, the most likely substitution. With this in mind it becomes apparent that most SNPs describe rare changes. The most likely ones only have values of 1 which does not seem much, compared to the maximum of 11, however the 75% quantile of all values in the matrix is only 0, so a value of 1 is not as low as it might seem. On the other hand a value of 1 seems a lot considering that E482K is a change from a negatively charged amino acid to a positively charged one.
In PAM1 and PAM250, the minimum and maximum values are 0,9976 and 9,72 respectively. The high maximum especially for PAM1, stems from the diagonal of the matrix; the 90% quantile is only 22. As to be expected, given more time for the changes to occur (PAM1 to PAM250), the probability for the substitutions to occur rises. However the general relation between the SNPs mostly stays comparable. The biggest change occurs for M1V which is seemed much more likely given only one unit of time.
From this small example the two matrices seem to correlate well. All values that are high in BLOSUM62 are comparably high in both PAM matrices as well, the only exception being M1V which becomes significantly less probable in PAM250. Concerning the phenotype it would be rather far fetched to make an assumption based only on these values, especially since they are mostly close together. However both matrices seem to agree that if there are disease causing SNPs among the set, they are M1V, L451V and E482K, possibly R178H as well.
PSSM
PSI-Blast was used to create a PSSM that should contain more refined mutation scores since they are calculated separately for each position. The resulting scores are shown in <xr id="tab:pssm"/>. Comparing the values to the scores from position-independent matrices used above one can see that the general relations between the SNPs mostly stay the same. Exception are M1V, L451V and E482K becoming less likely and R178H becoming more likely.
The change in M1V is to be expected considering that the first position in the homologous sequences should mostly be occupied by methionine due to the start codon. The fact that R178H becomes more likely is surprising since this position is one of those originally identified to be potentially important for catalysis. This might be a small indicator that the original assumption was wrong or that it at least is not important in all homologs.
L451 is found in a single residue loop that could also be seen as an extension of the preceding alpha-helix and is directly followed by a beta-strand. In any case the side-chain at this position is pointing outside of the protein and with only beta-sheets and alpha-helices nearby it does not seem to fulfil a deeper purpose. Therefore exchanging the leucine against the even comparably similar valine should not be a problem and the decreased score is a surprise that cannot be explained for now. Apparently the residue does serve a purpose in other seqeunces. E482K is again an expected change, since there is not only the change in charge but structural analysis also suggests that glutamate forms hydrogen bonds to neighbouring residues.
<figtable id="tab:pssm">
| Mutation | PSI-Blast PSSM |
|---|---|
| M1V | -6 |
| L39R | -4 |
| C58Y | -2 |
| L127R | -7 |
| R170W | -5 |
| R178H | 3 |
| S210F | 1 |
| D258H | 1 |
| L451V | -2 |
| E482K | -2 |
</figtable>
Multiple sequence alignments
And another step close to evolution: Identify all mammalian homologous sequences. Create a multiple sequence alignment for them with a method of your choice. Using this you can now calculate conservation for WT and mutant residues again. Compare this to the matrix- and PSSM-derived results.
<figtable id="tab:pssm_msa">
| Mutation | MSA-PSSM |
|---|---|
| M1V | 2 |
| L39R | -1 |
| C58Y | 0 |
| L127R | -3 |
| R170W | |
| R178H | |
| S210F | |
| D258H | |
| L451V | |
| E482K |
</figtable>
Prediction
SIFT
SIFT prediction is based on the degree of conservation of amino acid residues in sequence alignments derived from closely related sequences, collected through PSI-BLAST. The output states whether a SNP affects the protein function or not. Added are the score and also the number of sequences for the conservation calculation at this position <ref name ="sift">Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073-81. </ref>.
The score does not seem to take factors like the number of sequences into account, however the webserver warns, that there is low confidence in the prediction for M1V, since there is only one sequence (the query) for this SNP.
The scaled probabilities from the full output suggest that for each position of interest, there are no mutations tolerated at all, and any substitution amino acid should exert an effect. Apparently the positions in question are well conserved in the homologous sequences retrieved by SIFT.
The number of sequences at this position are factored into this value <ref name="siftpub">Ng,P. (2001) Predicting deleterious amino acid substitutions. Genome research, 863-874.</ref>.
<figtable id="tab:sift">
| Mutation | Prediction | Score | Sequences |
|---|---|---|---|
| M1V | AFFECT PROTEIN FUNCTION | 0.00 | 1 |
| L39R | AFFECT PROTEIN FUNCTION | 0.00 | 54 |
| C58Y | AFFECT PROTEIN FUNCTION | 0.00 | 55 |
| L127R | AFFECT PROTEIN FUNCTION | 0.00 | 67 |
| R170W | AFFECT PROTEIN FUNCTION | 0.00 | 66 |
| R178H | AFFECT PROTEIN FUNCTION | 0.00 | 65 |
| S210F | AFFECT PROTEIN FUNCTION | 0.00 | 68 |
| D258H | AFFECT PROTEIN FUNCTION | 0.00 | 68 |
| L451V | AFFECT PROTEIN FUNCTION | 0.00 | 66 |
| E482K | AFFECT PROTEIN FUNCTION | 0.00 | 65 |
</figtable>
PolyPhen2
Polyphen2 incorporates a multiple sequence alignment pipeline and a probabilistic classifier based on machine-learning for the annotation of SNPs. The method uses three strucutre-based and eight sequence-based predictive features. Two datasets were used to train and test PolyPhen2, HumDiv and HumVar <ref name="pph2">Peshkin,L. et al. (2010) A method and server for predicting damaging missense mutations. Nature methods, 7, 248-249.</ref>.
The results of the effect prediction by PolyPhen2 are shown in <xr id="tab:pph2"/>. As can be seen all SNPs but M1V are predicted to have a damaging effect with very high confidence. In fact the false positive rates for these probabilities are below 0.1. Obserbing the full output it can be seen that PolyPhen2 used structural information from PDB entry 2gk1 for all mutations but M1V, where no structure is available. In addition UniProtKB sequence annotations were used, marking C58 as being part of a disulfide bridge as well as noting that M1V is part of the signal peptide and L39 and C58 are part of a propeptide. Whether these predictions are therefore more reliable remains to be seen at the end when checking the real effect in HGMD, however only from the scores given by PolyPhen2 this would not be apparent, since many other mutations without these additional features have an equally high probability as well as FDR, FPR, TPR.
TODO 2 datasets humdiv humvar ?
<figtable id="tab:pph2">
| Mutation | Prediction | pph2_class | pph2_prob |
|---|---|---|---|
| M1V | benign | neutral | 0.063 |
| L39R | probably damaging | deleterious | 1 |
| C58Y | probably damaging | deleterious | 1 |
| L127R | probably damaging | deleterious | 1 |
| R170W | probably damaging | deleterious | 1 |
| R178H | probably damaging | deleterious | 1 |
| S210F | probably damaging | deleterious | 1 |
| D258H | probably damaging | deleterious | 1 |
| L451V | probably damaging | deleterious | 0.994 |
| E482K | probably damaging | deleterious | 1 |
</figtable>
SNAP
Finally, we use three different approaches to score our mutants.
SIFT
Polyphen2
SNAP is installed on the student cluster and should be used command-line only. You will need to create your own ~/.snapfunrc (unless Tim will change the default one) to point to the correct paths. -- As blast is the bottleneck of SNAP, and you are doing that anyway, we might as well look at all possible substitutions in the position of our mutations. This way we can learn much more about the nature of the given mutation: Is our mutation problematic because we introduce an unwanted effect, or because the WT residue is essential and by mutating we remove that?
SNAP was developed by Bromberg et al. in 2007 <ref name="yana">Yana Bromberg and Burkhard Rost. Snap: predict effect of non-synonymous
polymorphisms on function. Nucleic Acids Res, 35(11):3823–3835, 2007.</ref>. The method is based on a neural network with many input features such as SWISS-PROT annotations, biochemical properties, sequence information, PSI-BLAST profiles and predicted 1D-structure.
As input SNAP needs only sequence information but will also consider and benefit of available functional and structural annotations.
The prediction can be either that the SNP is "neutral" or "non-neutral".
SNAP is stated to have a 77% accuracy of non neutral predictions and 76% of the neutral substitutions at 80% accuracy.
The reliability index RI indicates the confidence of a certain prediction and should be considered together with the expected accuracy.
The SNAP prediction results are displayed in <xr id="tab:snap"/>. 4 out of the 10 SNPs are predicted to be neutral but only in the case of M1V the reliability and accuracy is very high. With E482K and S210F the reliability is below 2. The non neutral prediction for L39R and R178H are also comparably weak.
M1 seems to be a unimportant residue as it has 0 effect predictions at this position. 258, 178 and 58 seem to be the worst positions for any mutation whereas at 127 the chance of an effect is just around 50%.
<figtable id="tab:snap">
| Mutation | Prediction | RI | Accuracy | Non-neutral at this position |
|---|---|---|---|---|
| M1V | neutral | 9 | 91% | 0 |
| L39R | non-neutral | 2 | 63% | 10 |
| C58Y | non-neutral | 4 | 71% | 16 |
| L127R | non-neutral | 2 | 63% | 7 |
| R170W | non-neutral | 7 | 80% | 15 |
| R178H | non-neutral | 1 | 60% | 16 |
| S210F | neutral | 0 | 51% | 1 |
| D258H | non neutral | 3 | 67% | 16 |
| L451V | neutral | 6 | 79% | 9 |
| E482K | neutral | 2 | 59% | 2 |
</figtable>
Consensus
Compare ALL results and create an overview table. Try to come up with a consensus between all the findings requested above.
Evaluation
Check whether you are right in the HGMD – were you able to predict a change?
For this task it is very important to us that you properly interpret and discuss your results. The production of the data should not take that long – so you have more time to do real science!
References
<references/>
