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.

Mutations

Dataset

The following SNPs, selected by an unbiased source, will be analysed: M1V, L39R, C58Y, L127R, R170W, R178H, S210F, D258H, L451V and E482K.

   Pick 10 mutations (SNPs) of your dataset, some of which are from the HGMD (missense mutations) and some that were only found in dbSNP ( change in amino acid sequence but not found in the HGMD). Shuffle them and PLEASE do not try to memorize whether they cause the disease! The goal is to pretend that we do NOT know what is going on. It would be great if the most common disease-causing mutations would be included, too.

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 and the according category, the volume with the matching characterisation, the charge and the grantham score.
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>.

TODO: Cool stuff, where did volume come from and which of the many many hydrophobicity scales is this?

<figtable id="tab:biochem">

Table 1: Biochemical properties

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

   Now take into consideration where in the protein the mutation occurs and document: Create a picture with PyMOL showing the original and mutated residue in the protein. Use PyMOL for this. More thorough structural analyses will be introduced in the next task.

 Using your secondary structure predictions from the previous tasks, investigate whether the mutations are inside secondary structure elements (Helix, Strand) or not.

Substitution matrices

<figtable id="tab:pamnlos">

Table TODO: Substitution scores

Mutation	Blossum 62	Pam (1/250)
M1V
L39R
C58Y
L127R
R170W
R178H
S210F
D258H
L451V
E482K

</figtable>

   Look at the BLOSUM62 and PAM(1/250) matrix. What are the scores for the amino acid substitutions? Is it the worst possible substitution or not? Can we say anything about phenotype from this?
   Getting a bit closer to evolution you will have to create a PSSM (position specific scoring matrix) for your protein sequence using PSI-BLAST (5 iterations). How conserved are the WT residues in your mutant positions? How is the frequency of occurrence (conservation) for the mutant residue type? Anything interesting?

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.

Prediction

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? 

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!