(508f) Novel Framework for Beta-Sheet Topology Prediction Using Low-Homology Template-Based Constraints

Authors: 
Kieslich, C. A., Texas A&M University
Smadbeck, J., Princeton University
Khoury, G. A., Pennsylvania State University-University Park
Floudas, C. A., Princeton University



Accurate prediction of beta-sheet topology is a major unresolved challenge in the area of protein structure prediction. Current state-of-art approaches utilize sequence alignments, secondary structure assignments, and pairwise potentials to derive rank ordered lists of solutions [1-3].  Due to the large combinatorial complexity that arises for even a small number of beta strands, Mixed Integer Linear optimization (MILP) models have been proposed to identify the optimum topology [1-2]. Involvement of non-local tertiary contacts can make the prediction of beta-sheets based on sequence information alone very difficult. Therefore, we propose a novel framework for beta-sheet topology prediction, which utilizes structural templates of low-homology to derive likely beta-strand pairs that serve as constraints for a MILP model.

Given a query sequence, structural templates are first identified using a modified version of SPARKS-X [4]. Distance constraints are extracted from each template and are used as input for CYANA [5] to generate structural models. The initial set of structural templates is reduced using hierarchical clustering based on pairwise GDT, a measure of protein structure similarity. The beta-sheet topology of each template structure is extracted based on sequence alignments to the query and secondary structure assignment. A final set of structural templates is selected based on clustering of the template beta-sheet topologies, and a set of observed strand pairs is obtained.

The presented MILP model utilizes pairwise potentials calculated by BetaPro [6], as well as template-based constraints derived as described above. Additional constraints, initially proposed by Subramani and Floudas [2],  are also imposed to ensure that only biologically relevant topologies are generated. Ultimately, a rank-ordered list of likely beta-sheet topologies is produced. We present results for the application of the proposed framework to all beta and mixed alpha-beta proteins of the PDBSelect25 data set, as well as to the most difficult targets from recent CASP competitions.              

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