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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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Subramani, Ashwin | - |
dc.contributor.author | Floudas, Christodoulos A | - |
dc.date.accessioned | 2021-10-08T19:58:59Z | - |
dc.date.available | 2021-10-08T19:58:59Z | - |
dc.date.issued | 2012-03-09 | en_US |
dc.identifier.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 | en_US |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr15p1f | - |
dc.description.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/. | en_US |
dc.format.extent | e32461 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | PLoS ONE | en_US |
dc.rights | Final published version. This is an open access article. | en_US |
dc.title | β-sheet Topology Prediction with High Precision and Recall for β and Mixed α/β Proteins | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | doi:10.1371/journal.pone.0032461 | - |
dc.date.eissued | 2012-03-09 | en_US |
dc.identifier.eissn | 1932-6203 | - |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/journal-article | en_US |
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BSheetTopologyPredictHighPrecisionRecall.pdf | 189.29 kB | Adobe PDF | View/Download |
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