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Computation 2018, 6(2), 39; https://doi.org/10.3390/computation6020039

An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

1
Department of Computer Science, George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA 22030, USA
2
Department of Statistics, George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA 22030, USA
3
Department of Bioengineering, George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA 22030, USA
4
School of Systems Biology, George Mason University, 4400 University Drive, MS 4A5, Manassas, VA 20110, USA
*
Author to whom correspondence should be addressed.
Received: 22 April 2018 / Revised: 11 June 2018 / Accepted: 14 June 2018 / Published: 19 June 2018
(This article belongs to the Special Issue Computation in Molecular Modeling)
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Abstract

The energy landscape, which organizes microstates by energies, has shed light on many cellular processes governed by dynamic biological macromolecules leveraging their structural dynamics to regulate interactions with molecular partners. In particular, the protein energy landscape has been central to understanding the relationship between protein structure, dynamics, and function. The landscape view, however, remains underutilized in an important problem in protein modeling, decoy selection in template-free protein structure prediction. Given the amino-acid sequence of a protein, template-free methods compute thousands of structures, known as decoys, as part of an optimization process that seeks minima of an energy function. Selecting biologically-active/native structures from the computed decoys remains challenging. Research has shown that energy is an unreliable indicator of nativeness. In this paper, we advocate that, while comparison of energies is not informative for structures that already populate minima of an energy function, the landscape view exposes the overall organization of generated decoys. As we demonstrate, such organization highlights macrostates that contain native decoys. We present two different computational approaches to extracting such organization and demonstrate through the presented findings that a landscape-driven treatment is promising in furthering research on decoy selection. View Full-Text
Keywords: protein structure prediction; conformational space; energy landscape; decoy selection; dimensionality reduction; kernel regression protein structure prediction; conformational space; energy landscape; decoy selection; dimensionality reduction; kernel regression
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Akhter, N.; Qiao, W.; Shehu, A. An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction. Computation 2018, 6, 39.

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