?-sheet topology prediction with high precision and recall for ? and mixed ?/? proteins.

ABSTRACT: The prediction of the correct ?-sheet topology for pure ? and mixed ?/? proteins is a critical intermediate step toward the three dimensional protein structure prediction. The predicted beta sheet topology provides distance constraints between sequentially separated residues, which reduces the three dimensional search space for a protein structure prediction algorithm. Here, we present a novel mixed integer linear optimization based framework for the prediction of ?-sheet topology in ? and mixed ?/? proteins. The objective is to maximize the total strand-to-strand contact potential of the protein. A large number of physical constraints are applied to provide biologically meaningful topology results. The formulation permits the creation of a rank-ordered list of preferred ?-sheet arrangements. Finally, the generated topologies are re-ranked using a fully atomistic approach involving torsion angle dynamics and clustering. For a large, non-redundant data set of 2102 ? and mixed ?/? proteins with at least 3 strands taken from the PDB, the proposed approach provides the top 5 solutions with average precision and recall greater than 78%. Consistent results are obtained in the ?-sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The ?-sheet topology prediction algorithm, BeST, is available to the scientific community at http://selene.princeton.edu/BeST/.

SUBMITTER: Subramani A 

PROVIDER: S-EPMC3302896 | biostudies-other | 2012

REPOSITORIES: biostudies-other