Task homologyModelling

For the sequences used in this practical, protein structures have been determined. However, in real-life projects, you often do not have structures. Therefore, we will use structure prediction methods to predict the 3D structures of our sequences. We will also check whether and how the SNPs change the predicted structures.

Theoretical background talks

We will be looking at two parts of homology modelling

• Identifying suitable templates and producing and alignment
• Calculating the actual models and evaluating the results

Please include these programs in your talks

• HHpred
• Swissmodel
• Modeller
• iTasser

The talks should cover

• how the methods work behind the scenes,
• some information on their performance, strengths and weaknesses (as e.g. seen in CASP),
• a brief intro into how to call them and where to find documentation,
• some discussion how to evaluate the resulting models

Calculation of models

• Get an overview of available homologous structures based on the sequence searches and alignments. -- Here you can build on your searches from the alignment task (2).
  • If you have not found remote homologues before, then use HHsearch and/or COMA to check whether you can extend your list towards more remotely similar structures
• Divide your homologous structures into three groups at
  • > 80% sequence identity
  • 40% - 80% sequence identity
  • < 30% sequence identity (ideally go towards 20%)
• If possible (i.e. if there are structures at that level of sequence identity) create models using one template from each of the groups with
  • Modeller (command line)
    • The students from last year wrote a basic tutorial on all necessary steps for using Modeller for this task.
  • Swissmodel
  • iTasser (online)
• Try out what happens if you change the input settings. Therefore:
  • Use the default settings of the methods, i.e. use the standard workflow and directly feed the alignments to the modelling step
  • In addition: Have a look at the alignments you use for modelling.
    • Collect sequence-based information (important residues, sequence family profiles, secondary structure prediction, etc.) to check the alignment.
    • Edit the alignment.
    • Then, proceed with modelling.
    • Document what you changed and why.
  • In addition (if possible due to availability of templates): For modelling with Modeller: Use more than one template in one modelling step. - Explore different combinations of templates:
    • several close homologues (> 80% sequence identity)
    • several distant homologues (< 30% sequence identity)
    • one close and one distant homologue
    • What would you expect with respect to model quality? -- In the evaluation (see below), check whether you can see the expected trend.
• Feed your 5 (subjectively best) models into 3D-Jigsaw to get out recombined, optimised (?) models. -- Do this separately for the different categories of templates.

Now, you should have quite a large number of models.

Evaluate your models

• Check the numeric evaluation of your models (scores given by the modelling programs)
• Compare the models to the experimental structure (Select one apo and one complex structure if there are several experimental structures, document your choice of reference)
  • Look at your models!
  • Calculate the TM score of the models (use TMscore or TMS in /apps/bin/).
  • Calculate the C_alpha RMSD of the models (use SAP in /apps/bin/).
  • Extra diligence task: define a radius of 6 Angstrom around the catalytic centre and calculate the all atom RMSD in that region
• Discuss your results:
  • How do the RMSD and the TM score correlate? Is one score more helpful in finding meaningful models?
  • Do you see any correlation between the quality scores and the RMSD/TM score? (You do not need to calculate correlation coefficients, a qualitative estimation is enough.)
  • Is any method systematically better at predicting the structure?
  • Does this depend on the similarity of the template?
  • Does refining the alignments by hand help?