ArticleBiophysical journal2013
Inherent relationships among different biophysical prediction methods for intrinsically disordered proteins.
Article in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- On the Roles of Protein Intrinsic Disorder in the Origin of Life and Evolution.Life (Basel, Switzerland) · 2024Review
- Functional Bacterial Amyloids: Understanding Fibrillation, Regulating Biofilm Fibril Formation and Organizing Surface Assemblies.Molecules (Basel, Switzerland) · 2022Review
- Dimensions, energetics, and denaturant effects of the protein unstructured state.Protein science : a publication of the Protein Society · 2016Article
- Binding cavities and druggability of intrinsically disordered proteins.Protein science : a publication of the Protein Society · 2015Article
- Advantages of proteins being disordered.Protein science : a publication of the Protein Society · 2014Review
- A decade and a half of protein intrinsic disorder: biology still waits for physics.Protein science : a publication of the Protein Society · 2013Review
- Ligand clouds around protein clouds: a scenario of ligand binding with intrinsically disordered proteins.PLoS computational biology · 2013Article
- Polycation-π interactions are a driving force for molecular recognition by an intrinsically disordered oncoprotein family.PLoS computational biology · 2013Article
- Digested disorder: Quarterly intrinsic disorder digest (January/February/March, 2013).Intrinsically disordered proteinsReview
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrinsically disordered proteins do not have stable secondary and/or tertiary structures but still function. More than 50 prediction methods have been developed and inherent relationships may be expected to exist among them. To investigate this, we conducted molecular simulations and algorithmic analyses on a minimal coarse-grained polypeptide model and discovered a common basis for the charge-hydropathy plot and packing-density algorithms that was verified by correlation analysis. The correlation analysis approach was applied to realistic datasets, which revealed correlations among some physical-chemical properties (charge-hydropathy plot, packing density, pairwise energy). The correlations indicated that these biophysical methods find a projected direction to discriminate ordered and disordered proteins. The optimized projection was determined and the ultimate accuracy limit of the existing algorithms is discussed.
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