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- W2168781610 endingPage "5094" @default.
- W2168781610 startingPage "5081" @default.
- W2168781610 abstract "Intrinsically disordered proteins (IDPs) are a class of proteins lacking a well-defined secondary structure. Instead, they are able to attain multiple conformations, bind to multiple targets, and respond to changes in their surroundings. Functionally, IDPs have been associated with molecular recognition, cell regulation, and signal transduction. The dynamic conformational ensemble of IDPs is highly environmental and binding partner dependent, rendering the characterization of IDPs extremely challenging. Here, we compare the sampling efficiencies of conventional molecular dynamics (MD), well-tempered metadynamics (WT-META), and bias-exchange metadynamics (BE-META). The total simulation time was over 10 μs, and a 20-mer peptide derived from the Neh2 domain of the Nuclear factor erythroid 2-related factor 2 (Nrf2) protein was simulated. BE-META, with a neutral replica and seven biased replicas employing a set of seven relevant collective variables (CVs), provided the most reliable and efficient sampling. Finally, we propose a free-energy reconstruction method based on the probability distribution of the secondary structure contents. This postprocessing analysis confirms the presence of not only the β-hairpin conformation of the free Neh2 peptide but also its rare bound-state-like conformation, both of that have been experimentally observed. In addition, our simulations also predict other possible conformations to be verified with future experiments." @default.
- W2168781610 created "2016-06-24" @default.
- W2168781610 creator A5034852833 @default.
- W2168781610 creator A5071383926 @default.
- W2168781610 creator A5074870997 @default.
- W2168781610 date "2014-10-23" @default.
- W2168781610 modified "2023-09-25" @default.
- W2168781610 title "Accelerating the Conformational Sampling of Intrinsically Disordered Proteins" @default.
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- W2168781610 doi "https://doi.org/10.1021/ct5004803" @default.
- W2168781610 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26584388" @default.