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PredictingDisorder

Exercises Using the same sequence as before generate disorder predictions for your sequence. Data and sequences for exercises

Using IUPred generate a disorder prediction. For your sequence, which type of disorder would you expect to see?

Run the sequence now with the DisProt server using the different algorithms. Do they agree - if so, would you expect them to - if not - why not?

Comparing the output of these to the output of IUPred, explain the differences?

Compare using IUPred the results of the long prediction, short prediction for your protein?

How well does the globular prediction of IUPred compare to the globular predictors from earlier?

Some 3D structures are the result of residues that are not primary structure neighbours. How do these disorder algorithms take into account long range effects?

What effect does the window size for smoothing have on the prediction? Run your target sequence in IUPredwith the long and short window. Or use GlobPlot and alter the parameters to see what effect this has on the prediction.

How do the disorder predictions line up with the globular domain predictions from earlier on your protein?

In particular, contrast the scoring of residues in disprot with globplot. Which is better and why?

Using p53_human, run GlobPlot and search for domains on Pfam using whichever tool you find easiest (i.e. Pfam, DAS). How well do the predictions agree?

Repeat these exercises this time with the Breakless protein. Compare this with the prediction for Insulin Receptor Protein.

What characteristics do you think the machine learning algorithms such as disprot might be picking up?

If we still have time at the end:

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Page last modified on January 29, 2008, at 02:26 PM CET