Conformational Search for the Protein Native State

Shehu, Amarda

doi:10.1002/9780470882207.ch19

Cited by 18 publications

(14 citation statements)

References 82 publications

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“…Please note that we are explicitly stating that there may be many basins containing native conformations, rather than one unique basin. Typically, in protein structure prediction, it is assumed that the native conformational state of a protein is homogeneous and contains similar conformations (corresponding to one basin) [72]. However, there is a growing realization in protein structure modeling and CASP, stemming from many biological studies [73], that one needs to consider the multiplicity of native conformations; that is, a protein may utilize different, biologically-active conformational states that correspond to different basins in the landscape [16].…”

Section: The Energy Landscapementioning

confidence: 99%

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

2018

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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.

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Section: The Energy Landscapementioning

confidence: 99%

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

2018

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“…Many proteins are found to employ both small-scale and large-scale fluctuations to switch between different functional states, effectively modulating function [3]. However, the narrowed focus in template-free structure prediction is relevant for many proteins and has led to many advances in computational structural biology [17,34,35,40].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface

Olson

Shehu

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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We present an evolutionary stochastic search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local minima. This objective is of primary importance in protein structure modeling, whether the goal is to obtain a broad view of potentially different structural states thermodynamically available to a protein system or to predict a single representative structure of a unique functional native state. In this paper, we focus on the latter setting, and show how approaches from evolutionary computation for effective stochastic search and multi-objective analysis can be combined to result in protein conformational search algorithms with high exploration capability. From a broad computational perspective, the contributions of this paper are on how to balance global and local search of some highdimensional search space and how to guide the search in the presence of a noisy, inaccurate scoring function. From an application point of view, the contributions are demonstrated in the domain of template-free protein structure prediction on the primary subtask of sampling diverse low-energy decoy conformations of an amino-acid sequence. Comparison with the approach used for decoy sampling in the popular Rosetta protocol on 20 diverse protein sequences shows that the evolutionary algorithm proposed in this paper is able to access lower-energy regions with similar or better proximity to the known native structure.

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“…The complexity of the protein energy surface still presents a significant challenge, especially on mediumsize proteins [2]. For this reason, research into development and analysis of stochastic optimization algorithms for conformational search abounds [7].…”

Section: Introductionmentioning

confidence: 99%

Efficient basin hopping in the protein energy surface

Olson

Shehu

2012

2012 IEEE International Conference on Bioinformatics and Biomedicine

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The vast and rugged protein energy surface can be effectively represented in terms of local minima. The basin-hopping framework, where a structural perturbation is followed by an energy minimization, is particularly suited to obtaining this coarse-grained representation. Basin hopping is effective for small systems both in locating lower-energy minima and obtaining conformations near the native structure. The efficiency decreases for large systems. Our recent work improves efficiency on large systems through molecular fragment replacement. In this paper, we conduct a detailed investigation of two components in basin hopping, perturbation and minimization, and how they work in concert to affect the sampling of near-native local minima. We show that controlling the magnitude of perturbation jumps is related to the ability to effectively steer the exploration towards conformations near the protein native state. In minimization, we show that a simple greedy search is just as effective as Metropolis Monte Carlobased minimization. Finally, we show that an evolutionaryinspired approach based on the Pareto front is particularly effective in reducing the ensemble of sampled local minima and obtains a simpler representation of the probed energy surface.

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Conformational Search for the Protein Native State

Cited by 18 publications

References 82 publications

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

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

Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface

Efficient basin hopping in the protein energy surface

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