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

Author(s): Subramani, Ashwin; Floudas, Christodoulos A

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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
Publication Date: 9-Mar-2012
Electronic Publication Date: 9-Mar-2012
Citation: Subramani, Ashwin, and Christodoulos A. Floudas. "β-sheet Topology Prediction with High Precision and Recall for β and Mixed α/β Proteins." PloS one 7, no. 3 (2012): e32461. doi: 10.1371/journal.pone.0032461
DOI: doi:10.1371/journal.pone.0032461
EISSN: 1932-6203
Pages: e32461
Type of Material: Journal Article
Journal/Proceeding Title: PLoS ONE
Version: Final published version. This is an open access article.

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