4,027 research outputs found

    Electrostatics in the Stability and Misfolding of the Prion Protein: Salt Bridges, Self-Energy, and Solvation

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    Using a recently developed mesoscopic theory of protein dielectrics, we have calculated the salt bridge energies, total residue electrostatic potential energies, and transfer energies into a low dielectric amyloid-like phase for 12 species and mutants of the prion protein. Salt bridges and self energies play key roles in stabilizing secondary and tertiary structural elements of the prion protein. The total electrostatic potential energy of each residue was found to be invariably stabilizing. Residues frequently found to be mutated in familial prion disease were among those with the largest electrostatic energies. The large barrier to charged group desolvation imposes regional constraints on involvement of the prion protein in an amyloid aggregate, resulting in an electrostatic amyloid recruitment profile that favours regions of sequence between alpha helix 1 and beta strand 2, the middles of helices 2 and 3, and the region N-terminal to alpha helix 1. We found that the stabilization due to salt bridges is minimal among the proteins studied for disease-susceptible human mutants of prion protein

    Practical rare event sampling for extreme mesoscale weather

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    Extreme mesoscale weather, including tropical cyclones, squall lines, and floods, can be enormously damaging and yet challenging to simulate; hence, there is a pressing need for more efficient simulation strategies. Here we present a new rare event sampling algorithm called Quantile Diffusion Monte Carlo (Quantile DMC). Quantile DMC is a simple-to-use algorithm that can sample extreme tail behavior for a wide class of processes. We demonstrate the advantages of Quantile DMC compared to other sampling methods and discuss practical aspects of implementing Quantile DMC. To test the feasibility of Quantile DMC for extreme mesoscale weather, we sample extremely intense realizations of two historical tropical cyclones, 2010 Hurricane Earl and 2015 Hurricane Joaquin. Our results demonstrate Quantile DMC's potential to provide low-variance extreme weather statistics while highlighting the work that is necessary for Quantile DMC to attain greater efficiency in future applications.Comment: 18 pages, 9 figure
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