(388e) Novel Biclustering Framework for Tertiary Contact Prediction Using Low-Homology Protein Templates

The prediction of the three-dimensional structure of a protein from its amino acid sequence remains an open question in molecular biology, with important implications in biological engineering. For sequences with low-homology template structures, the ranking of template structures and generation of predicted structures can be particularly difficult. The development of accurate prediction of long-range amino-acid contacts can be important in ranking these low-homology templates for modeling. Additionally, these contacts can be useful in constraining dynamic simulations of protein structure of low-homology targets. To this end we have developed a novel tertiary contact prediction method based on biclustering analysis for extracting C_α distance constraints from low homology templates.

The initial step of our procedure is the identification of template structures for the target sequence using a modified threading algorithm of SPARKS-X [1]. Preliminary models, based on the top template structures, are generated using CYANA [2] in order to remove gaps in the structures resulting from unmapped regions of the alignments. To identify persistent structures and topologies within the templates, hierarchical clustering based on pair-wise GDT (a structure similarity measure) is performed using the initial CYANA models. The template structures belonging to the largest three clusters of the GDT-based dendrogram tree are selected for C_α-C_α distance calculation. This matrix of C_α-C_α distances then serves as input to OREO[3-5], which is an iterative framework for biclustering dense and sparse data matrices via optimal re-ordering of rows and columns.

The final step is to filter the clustered distances to exclude distances of low confidence. We apply three filters: (i) a variance filter, based on the mean/standard deviation of each distance; (ii) a sequence mapping filter, in which contacts involving poorly mapped positions are excluded, according to an accumulation of the position-specific scoring matrix (PSSM) produced by SPARKS-X; (iii) a structure-based filter, which removes distance constraints that deviate significantly from the predicted values during CYANA structure generation. The structure-based filter includes additional constraints derived from CONCORD[6] predicted secondary structure and predicted beta-sheet topology[7], which is essential for identifying conflicting constraints. The remaining clustered distances are considered strong candidates for conserved contacts and are used to generate C_α distance constraints for a final structure generation.

We present results on a series of free-modeling targets from the Critical Assessment of techniques for protein Structure Prediction 10 (CASP10) competition. This method demonstrates a superior performance over other low-homology template-based contact prediction methods in prediction short, medium, and long-range contacts for difficult targets. The contacts are used in the generation of structures through a constrained molecular dynamics (MD) run to demonstrate how such contacts are important for the accurate structural fold determination.

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