A multi-directional rapidly exploring random graph (mRRG) for protein folding

Nath, Shuvra Kanti; Thomas, Shawna; Ekenna, Chinwe; Amato, Nancy M.

doi:10.1145/2382936.2382942

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…It is worth noting that the proposed algorithm bears some resemblance with robotics-inspired graph-or tree-based conformational search algorithms presented to sample the native state of a protein sequence [18][19][20], compute conformational paths connecting diverse functionally-relevant states of the same sequence [9,11,16], or obtain folding pathways of a protein given its native structure [17,24]. However, the only resemblance of consequence is the employment of a graph-like search structure, as other methods rely on specific representations and sampling strategies closely tied to the application at hand.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Structure Space of Wildtype Ras Guided by Experimental Data

Clausen

Shehu

2013

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

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The Ras enzyme mediates critical signaling pathways in cell proliferation and development by transitioning between GTP-(active) and GDP-bound (inactive) states. Many cancers are linked to specific Ras mutations affecting its conformational switching between active and inactive states. A detailed understanding of the sequence-structure-function space in Ras is missing. In this paper, we provide the first steps towards such an understanding. We conduct a detailed analysis of X-ray structures of wildtype and mutant variants of Ras. We embed the structures onto a low-dimensional structure space by means of Principal Component Analysis (PCA) and show that these structures are energetically feasible for wildtype Ras. We then propose a probabilistic conformational search algorithm to further populate the structure space of wildtype Ras. The algorithm explores a low-dimensional map as guided by the principal components obtained through PCA. Generated conformations are rebuilt in all-atom detail and energetically refined through Rosetta in order to further populate the structure space of wildtype Ras with energetically-feasible structures. Results show that a variety of novel structures are revealed, some of which reproduce experimental structures not subjected to the PCA but withheld for the purpose of validation. This work is a first step towards a comprehensive characterization of the sequence-structure space in Ras, which promises to reveal novel structures not probed in the wet laboratory, suggest new mutations, propose new binding sites, and even elucidate unknown interacting partners of Ras.

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Section: Introductionmentioning

confidence: 99%

Exploring the Structure Space of Wildtype Ras Guided by Experimental Data

Clausen

Shehu

2013

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

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Defining Low-Dimensional Projections to Guide Protein Conformational Sampling

Novinskaya

Devaurs

Moll

et al. 2017

Journal of Computational Biology

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Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.

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