Abstract
Motivation: Knots in polypeptide chains have been found in very few proteins, and consequently should be generally avoided in protein structure prediction methods. Most effective structure prediction methods do not model the protein folding process itself, but rather seek only to correctly obtain the final native state. Consequently, the mechanisms that prevent knots from occurring in native proteins are not relevant to the modeling process, and as a result, knots can occur with significantly higher frequency in protein models. Here we describe Knotfind, a simple algorithm for knot detection that is fast enough for structure prediction, where tens or hundreds of thousands of conformations may be sampled during the course of a prediction. We have used this algorithm to characterize knots in large populations of model structures generated for targets in CASP 5 and CASP 6 using the Rosetta homology-based modeling method. Results: Analysis of CASP5 models suggested several possible avenues for introduction of knots into these models, and these insights were applied to structure prediction in CASP 6, resulting in a significant decrease in the proportion of knotted models generated. Additionally, using the knot detection algorithm on structures in the Protein Data Bank, a previously unreported deep trefoil knot was found in acetylornithine transcarbamylase. Availability: The Knotfind algorithm is available in the Rosetta structure prediction program at Contact:bort@soe.ucsc.edu