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• W2080462962 abstract "We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa-Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation." @default.
• W2080462962 created "2016-06-24" @default.
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• W2080462962 date "2010-02-01" @default.
• W2080462962 modified "2023-09-25" @default.
• W2080462962 title "A novel approach for large-scale polypeptide folding based on elastic networks using continuous optimization" @default.
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• W2080462962 doi "https://doi.org/10.1016/j.jtbi.2009.10.010" @default.
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