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      Antimicrobial and Bioactive Silk Peptide Hybrid Hydrogel with a Heterogeneous Double Network Formed by Orthogonal Assembly

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          Canonical sampling through velocity rescaling

          The authors present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.
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            GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

            Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.
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              The MARTINI force field: coarse grained model for biomolecular simulations.

              We present an improved and extended version of our coarse grained lipid model. The new version, coined the MARTINI force field, is parametrized in a systematic way, based on the reproduction of partitioning free energies between polar and apolar phases of a large number of chemical compounds. To reproduce the free energies of these chemical building blocks, the number of possible interaction levels of the coarse-grained sites has increased compared to those of the previous model. Application of the new model to lipid bilayers shows an improved behavior in terms of the stress profile across the bilayer and the tendency to form pores. An extension of the force field now also allows the simulation of planar (ring) compounds, including sterols. Application to a bilayer/cholesterol system at various concentrations shows the typical cholesterol condensation effect similar to that observed in all atom representations.
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                Author and article information

                Contributors
                Journal
                ACS Biomaterials Science & Engineering
                ACS Biomater. Sci. Eng.
                American Chemical Society (ACS)
                2373-9878
                2373-9878
                January 10 2022
                December 03 2021
                January 10 2022
                : 8
                : 1
                : 89-99
                Affiliations
                [1 ]Institute for Frontier Materials, Deakin University, Geelong, Victoria 3200, Australia
                [2 ]Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, P. R. China
                Article
                10.1021/acsbiomaterials.1c01228
                34859992
                7d766cca-abae-49cb-8575-ec0c44abda30
                © 2022

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-045

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