β-sheet Topology Prediction with High Precision and Recall for β and Mixed α/β Proteins

The prediction of the correct [Image: see text]-sheet topology for pure [Image: see text] and mixed [Image: see text] 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 [Image: see text]-sheet topology in [Image: see text] and mixed [Image: see text] 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 [Image: see text]-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 [Image: see text] and mixed [Image: see text] 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 [Image: see text]-sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The [Image: see text]-sheet topology prediction algorithm, BeST, is available to the scientific community at http://selene.princeton.edu/BeST/.