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Exploration of conformational transition pathways from coarse-grained simulations

机译:从粗粒度模拟探索构象过渡途径

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Motivation: A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, ... ) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD).
机译:动机:提出了一种跟踪蛋白质构象转变的新算法。该方法使用离散分子动力学作为引擎来采样蛋白质构象空间。将多个极小Go类势能函数与几种增强的采样策略(例如元动力学,Maxwell Demon分子动力学和基本动力学)结合使用。该方法显示出空前的计算效率,能够追踪各种已知的实验转变。与更简单的方法相反,我们的策略不会在蛋白质的化学结构中引入扭曲,并且能够复制复杂的非线性构象转变。称为GOdMD的方法可以轻松地对过渡过程施加其他限制(配体的存在,已知的中间体,已知的保持的接触...),并且可以通过西班牙国家生物信息学研究所(http:// mmb)自由地分发给社区。 irbbarcelona.org/GOdMD)。

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