beta_helix Staff Lv 1
As soon as I get the sequence logo secondary structure predictions back from the SAM server
(http://compbio.soe.ucsc.edu/SAM_T08/faq.html)
I will post them here!
Closed since over 16 years ago
AdvancedThe third Mini-CASP target is another de-novo (or ab-initio) target. This means that there is no solved structure that has a similar sequence that we can start our predictions from. This is exciting because template-free targets are rare (in CASP8 only 11 of the 128 targets were free-modeling targets) so we are lucky to be able to predict this. Since we have no idea where to even start, we are giving you an extended chain to see what you come up with. This might not be easy, so we will release Rosetta predictions for this target as puzzles early next week. Until then, good luck freestyle folding! As suggested in the feedback (http://fold.it/portal/node/986548#comment-4998) we will post the secondary structure prediction in the puzzle comments.
As soon as I get the sequence logo secondary structure predictions back from the SAM server
(http://compbio.soe.ucsc.edu/SAM_T08/faq.html)
I will post them here!
Here is the sequence logo predicted by the SAM server.
H = helix
E = sheet
C = loop (or coil)
The taller the letter at each position, the higher the probability of that specific secondary structure for that amino acid.
For example, the amino acid Valine at residue 60 is highly predicted to be part of a beta sheet. However, the Valine at residues 51 is predicted to have an equal probability of being part of a sheet or part of a loop (so it's probably not a helix).
Thanks for that, it already helped :)
btw, maybe a stupid question, but how are these secondary structures predicted?
this website has a pretty good intro:
http://www.bmm.icnet.uk/people/rob/CCP11BBS/secstrucpred.html
if you want more information about the method used to generate these sequence logos,
you can check out section 6 in http://compbio.soe.ucsc.edu/SAM_T08/faq.html
and everything you ever wanted to know about Hidden Markov Models (which is what SAM uses):
http://compbio.soe.ucsc.edu/sam.html
I hope these help!
This will take some more research tomorrow, as it doesn't read like a bedtime story…I didn't even know I wanted to know anything about Hidden Markov Models, LOL.
I'll have fun learning though, no doubt, your links are very much appreciated!