Difference between revisions of "Task 6 - Sequence-based mutation analysis"

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The slides to the talk can be found [[File:Talk6_Sequence_based_mutation_analysis.pdf|here]] for review.
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The slides to the talk can be found here for review for review. [[File:Talk6_Sequence_based_mutation_analysis.pdf]]
   
 
== Tasks ==
 
== Tasks ==

Revision as of 13:06, 13 June 2012

All the proteins studied in this practical are involved in monogenetic diseases. These diseases can be caused by single point mutations. In this task we simulate the case that we do not know the effect of specific mutations and try to predict the effects from sequence alone.

Introductory talk

The following topics will be addressed in the talk:

  • General overview on amino acids and their physical/chemical properties
  • amino acid substitution matrices
  • SNAP
  • Polyphen
  • SIFT

The slides to the talk can be found here for review for review. File:Talk6 Sequence based mutation analysis.pdf

Tasks

In this task we try to learn about the effects mutations can have on protein function/stability, just by looking at sequence changes. For this we will employ several different tools, but also apply some methods you have been introduced to during the course of this practical.

  • 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.
  • The simplest approach is to look at the differences in the WT (wild-type) and mutant amino acids. Please write for each of the 10 mutations a short summary about the physicochemical properties and changes.
  • 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.
  • 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?
  • 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.
  • 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?
  • Compare ALL results and create an overview table.
  • Try to come up with a consensus between all the findings requested above.
  • 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