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698 (<150): Human cancer protein interaction network target
Status: Closed


Name: 698 (<150): Human cancer protein interaction network target
Status: Closed
Created: 04/05/2013
Points: 100
Expired: 04/17/2013 - 08:00
Difficulty: Beginner
Description: The solution structure of this dead ringer-like protein has already been solved using NMR, but the crystal structure has yet to be released. We are giving you all 20 models from the NMR ensemble. Every time you reset the puzzle it will cycle through these 20 structures and they are available in the Alignment Tool so you can use partial threading between them. We look forward to seeing how close to the crystal structure you are able to get! For players with fewer than 150 global points.
Categories: Overall, Prediction

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beta_helix's picture
User offline. Last seen 1 hour 18 min ago. Offline
Joined: 05/09/2008
Groups: None

If you are new to Foldit, here are detailed instructions to get you started:

Additional information on partial threading here:

If you want to fold proteins, know that there are many way to play and this variety is the main value of Foldit :
- try all puzzles not only "beginners" one,
- fold first the ones you like the most,
- try by yourself to understand what works and what doesn't,
- make your own strategy and at least try more than one, here some other strategies to play QTTN http://foldit.wikia.com/wiki/101_-_Puzzle_Levels_and_Types,
- ask for tips in global when you think you doesn't understand something.

Make sure you have followed these tutorials:
- lonely sheets,
- lock and lower,
- rebuild,
- structure and rebuild,
- control over clashing
before starting this puzzle.

marie_s's picture
User offline. Last seen 1 year 34 weeks ago. Offline
Joined: 05/18/2008
Groups: None
this puzzle is an alignment puzzle

So make sure you have also followed the tutorials:
- basic threading
- alignin sequence

you can also see on the wiki these pages :

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Developed by: UW Center for Game Science, UW Institute for Protein Design, Northeastern University, Vanderbilt University Meiler Lab, UC Davis
Supported by: DARPA, NSF, NIH, HHMI, Amazon, Microsoft, Adobe, Boehringer Ingelheim, RosettaCommons