ArticleJournal of computational biology : a journal of computational molecular cell biology2016
Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins.Protein science : a publication of the Protein Society · 2023Article
- Molecular Dynamics Ensemble Refinement of Intrinsically Disordered Peptides According to Deconvoluted Spectra from Circular Dichroism.Biophysical journal · 2020Article
- Intrinsically disordered proteins of viruses: Involvement in the mechanism of cell regulation and pathogenesis.Progress in molecular biology and translational science · 2020Review
- Recent Advances in Computational Protocols Addressing Intrinsically Disordered Proteins.Biomolecules · 2019Review
- Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data.Journal of computational biology : a journal of computational molecular cell biology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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Abstract
Modeling the structural ensemble of intrinsically disordered proteins (IDPs), which lack fixed structures, is essential in understanding their cellular functions and revealing their regulation mechanisms in signaling pathways of related diseases (e.g., cancers and neurodegenerative disorders). Though the ensemble concept has been widely believed to be the most accurate way to depict 3D structures of IDPs, few of the traditional ensemble-based approaches effectively address the degeneracy problem that occurs when multiple solutions are consistent with experimental data and is the main challenge in the IDP ensemble construction task. In this article, based on a predefined conformational library, we formalize the structure ensemble construction problem into a least squares framework, which provides the optimal solution when the data constraints outnumber unknown variables. To deal with the degeneracy problem, we further propose a regularized regression approach based on the elastic net technique with the assumption that the weights to be estimated for individual structures in the ensemble are sparse. We have validated our methods through a reference ensemble approach as well as by testing the real biological data of three proteins, including alpha-synuclein, the translocation domain of Colocin N, and the K18 domain of Tau protein.
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