Fold recognition using web-servers

Morten Nielsen (mniel@cbs.dtu.dk)

Claus Lundegaard (lunde@cbs.dtu.dk)

Overview

Fold recognition (FR) is the name given to the process of assigning a known structure (a template) to a sequence of unknown structure (the query). The methodologies used include:

• Straight sequence methods (like BLAST and PSI-BLAST)
• Sequence methods incorporating structure, including:
• predicted and known secondary structure
• structural environments
• structural alignments
• 'True' threading approaches

The difference between "sequence" based methods and methods using threading is not always clear. In principle the sequence based method defines the "fitness" of the query onto the template from on the primary structure of the query and template sequences, respectively. Threading methods on the other hand defines the "fitness" of the query from the structural environment of the template structure. However as you saw from the list above some sequence based methods also incorporates structural information of the template in the alignment so the borderline is not very clear. The most powerful method are neither "true" sequence based nor "true" threading method, but some mixture of the two.

Finding information

Note: If links are not given on this page, it's assumed that you'll use Google to find it

The exercise

Please mail the answers to all the questions asked in the exercise to: lund@cbs.dtu.dk

Below we give you a list of three protein sequences. You shall now try to use some tools to assign a fold and structure to each sequence. The sequences are placed in the categories CM (comparative modeling), CM/FR (comparative modeling/fold recognition), and NF (new fold). As the categories suggest, the CM is the easy class, the CM/FR the hard, and the NF the difficult (close to impossible) class. (Why is this?)

In the exercise you shall find out which of the three sequences belong to which of the three categories, and for the two sequences belonging to the CM and the CM/FR categories you shall find which template you should use to build an homology model (template recognition).

On to the BLAST web-site. Select Protein-protein BLAST (blastp). Blast the three sequences Query1, Query2, and Query3 against PDB. Note you do this by clicking on the Protein-protein BLAST, chose PDB from Choose database, and pasting in the sequences one by one. Remember to press FORMAT in formatting BLAST window.

• Q1 What are the E-values for the three searches?
• Q2 Are any of the hits significant (Eval < 0.001)?

Next use the PHI- and PHI-BLAST version of Blast. On to the BLAST web-site once more. Now select the Position-specific iterated and pattern-hit initiated BLAST (PHI- and PHI-BLAST) option. Paste in the query sequence Query1. Leave the database to nr, and press Blast. Again remember to press FORMAT one the formatting BLAST side.

• Q3 How many significant hits does blast find (E-value < 0.001)?
• Q4 How large a fraction of the query sequence does the significant hits match?
• Q5 Do you find any PDB hits among the significant hits (search for pdb in the hit list or look for the colored S to the right of the E-value))?

Now run a second Blast iteration. Press Run PSI-Blast iteration 2, go to the formatting BLAST window and press FORMAT.

• Q6 How many significant hits does Blast find (E-value < 0.001)?
• Q7 How large a fraction of the query sequence does the significant hits match?
• Make use you understand what is going on.
• Q8 Why does Blast come up with more significant hits in the second iteration?
• Q9 Do you find any PDB hits among the significant hits (search for pdb in the hit list or look for the red colored S to the right of the E-value)?
• Q10 What is the PDB identifier for the best PDB hit?

Repeat the PHI- PHI-Blast search for the other two query sequences.

Now you have probably found that one of the three protein targets could be modeled using sequence searches only, and this query is hence the easy one (the CM query). You shall now use some more advanced tools to try to model the last two sequences. There exist a large series of web-based protein model programs. Here we cannot go through them all. We normally focus on two servers. First Phyre (former 3D-PSSM), not because it performs better than the other programs (it is actually fare from being the best), but because it has a very nice and informative web-interface.

Since these are down we have chosen to use two other servers: HHpred and FFAS (chose servers then FFAS03).

http://toolkit.tuebingen.mpg.de/index.php?view=waiting&checkid=5103736&

To save you time, we have submitted the 3 sequences to the HHpred server. To see the output click on the following links Query1, Query2, Query3.

Spend some time looking at the results and make sure you understand what is going on.

• Q11 Try to classify the queries into CM/FR (hard), and NF (difficult)?
• Q12 What template does the Phyre server find for the hard (CM/FR) query?

You probably found that one of the two sequences could be modeled with a high certainty using the Phyre server. As in all other prediction games, you can often get a better idea of an answer to a question by asking the question to many different prediction servers. The list of public protein modeling servers is long. You can submit a query to a list of servers using the META-server. On the server you can submit a query sequence to a list of protein model prediction server simultaneously. The calculation takes some time, so we have submitted the sequences to the server beforehand. You find the output by clicking on the links Query2 and, Query3.

Check the Meta-server output for each of the two targets (we have left out Query 1). A powerful ways to combine the output from many prediction servers, is to extract a consensus prediction. The 3D-jury program does this. The 3d-jury calculates a score for the models predicted by the different servers, and reports a jury score. A value above 50 means a significant model. On the Meta server output webside you can select the set of servers you would like display (use the right selection window, and use Ctrl to select multiple methods). Select for instance FFAS03, 3D-PSSM, Blast, and PDB-Blast and compare the predictions.

• Q13 Can any of the sequences be modeled with a significant hit?
• Q14 Does the best hit found by the 3D-JURY and Phyre method agree (i.e. same name or SCOP class)?
• Q15 How do the top scoring jury hits differ in SCOP class?
• Q16 Can you classify the two sequences into hard (CM/FR) and difficult (NF)?
• Q17 Can you come up with a good guess for a template and a SCOP class for the CM/FR query?

Now you probably found that the fold of one to the difficult query sequences could be found using the jury approach where many different protein structure prediction servers are combined. The significance of hit you found and the corresponding SCOP class was very high even though the individual prediction servers could come up with (significant) hits beloning to very different structure classes!

And now a final philosophical question. What do you do if you cannot find a template?

Livebench

Now you have seen a real life protein fold recognition experiment. All the servers included in the Mete-server believe them self to be among the best in the world. Read about the Livebench project. Why is it important to assess fold recognition servers in this way? Click on the Set 9, and see which methods performed the best.

That's it for now!