Task alignments 2012

Most prediction methods are based on comparisons to related proteins. Therefore, the search for related sequences and the alignment to other proteins is a prerequisite for most of the analyses in this practical. Hence we will investigate the recall and alignment quality of different alignment methods.

Theoretical background talks

The introductory talks should given an overview of

• pairwise alignments and high-throuput profile searches (e.g. Fasta, Blast, PSI-Blast, HHsearch)
• multiple alignments (e.g. ClustalW, Probcons, Mafft, Muscle, T-Coffee, Cobalt) and MSA editors (e.g. Jalview)

with special attention to advantages and limitations of theses methods.

Sequence searches

Subsequently, for every native protein sequence for every disease the students shall employ different tools for database searching and multiple sequence alignment in the "big80" database. The methods to employ (minimally) are:

• Searches of the non-redundant sequence database big80:
  • Blast
  • PSI-Blast using standard parameters with all combinations of
    • 3 iterations
    • 10 iterations
    • default E-value cutoff (0.005)
    • E-value cutoff 10E-6
  • HHblits / HHsearch

Note: Check the outcome of your simple blast search. If there are many significant hits, increase the number of reported hits (-b or max_target_seqs depending on blast version) until no more relevant hits are found. Use that parameter also for the PSI-Blast searches and use a similar setting for HHblits / HHsearch. (Think about why we ask you to do this.)

For evaluating the differences of the search methods:

• compare the result lists (how much overlap, distribution of %identity and score)
• validate the result lists -- e.g.
  • using COPS to check whether found pdb entries fall into the same fold class
  • using GO to check whether sequences have common GO classifications

Multiple sequence alignments

Multiple sequence alignments of 20 sequences from the database search, including sequences from these ranges:

• 99 - 90% sequence identity
• 89 - 60% sequence identity
• 59 - 40% sequence identity
• 39 - 20% sequence identity

Ideally there should be 5 sequences from each range with at least one pdb-structure in each range. -- This will only be possible in rare cases!

The alignment methods to use are:

• ClustalW
• Muscle
• T-Coffee with
  • default parameters ("t_coffee your_sequences.fasta)
  • use of 3D-Coffee

Comparison of the alignments:

• How many conserved columns?
• Are functionally important residues conserved?
• How many gaps?
• Are there gaps in secondary structure elements?