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Structure prediction using true distance and angle constraint

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Structure prediction using true distance and angle constraint
#1

Hello,

I wonder if naive protein structure can be predicted by using true distance and angle constraints.
If we can do that, how do we choose rosetta applications and parameters?

Post Situation: 
Wed, 2019-08-21 02:29
maruhuang
Wed, 2019-08-21 15:29
smlewis

Hi, smlewis. Thanks for your reply.

I found that HybridizeMover requires a template. But I have no template for builting model.

I have tried to use abinitio-relax for structural prediction and added experimental constraint as follows:

AtomPair CB 63 CA 65 FLAT_HARMONIC 3.47 1.0 0.50
AtomPair CB 67 CB 69 FLAT_HARMONIC 6.47 1.0 0.50

....

Dihedral C 3 N 4 CA 4 C 4 CIRCULARHARMONIC 1.71 0.50
Dihedral N 4 CA 4 C 4 N 5 CIRCULARHARMONIC -2.33 0.50
Dihedral C 4 N 5 CA 5 C 5 CIRCULARHARMONIC -1.72 0.50

....

But the result is very different from the naive structure. I'm wondering what went wrong.

Any help would be appreciated.

Tue, 2019-08-27 01:21
maruhuang

There are a thousand places this could go wrong.  I don't actually know what is wrong - particularly I cannot parse "the result is very different from the naive structure". If you mean naive as constraint-free: I would expect that the constrained result should be very different, that's the point of constraints.  If you meant 'native', then I don't know why you don't have a template for hybridize.

If you are doing effectively ab initio with constraints, I would guess the constraints aren't strong enough during the early stages; perhaps they are not on at all if they are not active in the scorefunction.  I can guess another dozen things that might be wrong but I don't have enough data to tell.

You should probably re-ask this in a new thread; it's easier to get attention to unanswered posts and I clearly don't know enough about what you are trying to do to help.

Mon, 2019-09-16 14:10
smlewis

We have increased the weight of the constraint and this problem is solved.

Thank you very much!

Thu, 2019-09-26 18:52
maruhuang