Post on 16-Mar-2016
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KNOTFIND: A SIMPLE ALGORITHM FOR DETECTING KNOTS IN PROTEINS
Firas Khatib, Carol Rohl Department of Biomolecular Engineering
Can you tell which of these two
protein chains are knotted?
How can we tell if a protein chain is knotted?
This was the highest scoring model using Rosetta’s automated server Robetta for Target 202 in the
CASP6 blind prediction experiment.
Unfortunately this model is knotted. Knotted
Region
Can you tell which of these two
protein chains are knotted?
How can we tell if a protein chain is knotted?
KnotFree
This was the model submitted
by our group using the same alignment as Robetta. It is
devoid of knots.
• Knots in polypeptide chains have been found in very few proteins. [Nureki et al., 2002]
• Only 8 knots have been found in over 2000 known protein structures. [Taylor, 2000, 2002]
• We set out to write a program that checked modeled structures for knots.
Why Knots?
Detecting Knots without Knot Theory
• Joining the ends of a protein chain can create additional knots and cause other problems.
• In daily life we often encounter knots without closed loops: telephone cords, ethernet cables, extension cords, headphones…etc.
• We define such knots as: “pulling both ends results in a tangle that cannot be undone.”
Potential Knot
This is 1Ni5A. How can we find out if it is knotted?
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Rotating and moving
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Pulling Both Ends of the Chain
Simplifying
Simplifying
Pulling Both Ends of the Chain
N and C termini connected. Therefore 1Ni5A has no knot.
Pulling Both Ends of the Chain
• This concept of “pulling the ends of the chain”
was the basis for the Knotfind Algorithm.
• Knotfind goes through the chain three residues at
a time and simulates the pulling of the ends of the
chain in an attempt to simplify it.
The Knotfind Algorithm
Example of our Algorithm
First iteration through the chain
Example of our Algorithm
Therefore this chain has no knots
This chain cannot be simplified further, so
this is a knot.
In this final stage, all triangles of
three consecutive residues have line segments going through
them.
Example of our Algorithm with a knot
Localizing the Knot
But once we know a knot exists, how
do we find it?
Localizing the Knot
Knotfind specifies the region that
could not be simplified.
Localizing the Knot
Using Knotfind we are able to locate the knotted region of the chain.
Knotfind Results on CASP5 Experiment
After running Knotfind on all of Rosetta’s comparative modeling CASP5 decoy sets from University of Washington’s group:
Total number of chains : 45,366 Total number of knots : 2,163
4.8% of the decoys had knots.
Why do comparative modeling techniques lead to knots in some modeled structures?
We noticed three types of knots
• Two different loop regions linking with one another.
• A loop region threading itself through the template.
• A loop region knotting with itself:
This occurs extremely rarely and is likely the result of a high gap penalty.
• For CASP6 we filtered our loops:
– Libraries of loops were filtered before being placed onto the template.
– They were screened for knots with themselves as well as knots with the template.
How can we prevent knots?
Target 262
The green region is a loop region that conflicted with the template. Because of this, we selected a different alignment for this target.
How did we prevent knots in CASP6?
Robetta’s best model knots itself
with the template
One of our knotted decoys that we
discarded due to two conflicting loop
regions
Both models used the same template,
shown in blue
Knotfind Results after CASP6
After running Knotfind on all of Rosetta’s comparative modeling CASP6 decoy sets from UCSC:
Total number of chains : 117,200 Total number of knots : 1,300
1.1% of the decoys had knots.
For CASP5, 4.8% of the decoys had knots.
Conclusions
• The Knotfind algorithm is simple and fast.
• Knot formation correlates with breaks in the chain.
• Early filtering of loop libraries helps prevent the formation of knots.
• Future work: detecting regions that may not define a knot but are unproteinlike (such as tied shoelaces) .
Acknowledgements
Carol RohlJosue SamayoaDavid Bernick
Craig LoweMatt WeirauchPaul BourkeBrian Raney
Art LyubimovKevin Karplus