Predicting the conformational class of short and medium size loops connecting regular secondary structures:...

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.