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首页> 外文会议>International Conference on Bioinformatics and Biomedicine Workshops >Coarse-grained elastic networks, normal mode analysis and robotics-inspired methods for modeling protein conformational transitions
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Coarse-grained elastic networks, normal mode analysis and robotics-inspired methods for modeling protein conformational transitions

机译：粗粒子弹性网络，正常模式分析和机器人机制启发方法，用于建模蛋白质构象过渡

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This paper presents a method, inspired by robot motion planning algorithms, to model conformational transitions in proteins. The capacity of normal mode analysis to predict directions of collective large-amplitude motions is exploited to bias the conformational exploration. A coarse-grained elastic network model built on short fragments of three residues is proposed for the rapid computation of normal modes. The accurate reconstruction of the all-atom model from the coarse-grained one is achieved using closed-form inverse kinematics. Results show the capacity of the method to model conformational transitions of proteins within a few hours of computing time on a single processor. Tests on a set of ten proteins demonstrate that the computing time scales linearly with the protein size, independently of the protein topology. Further experiments on adenylate kinase show that main features of the transition between the open and closed conformations of this protein are well captured in the computed path.
机译：本文提出了一种由机器人运动规划算法的启发的方法，以模拟蛋白质化构象转变。采用正常模式分析的能力，以预测集体大幅度运动的方向，以偏置构象探索。提出了一种基于三个残留物的短片段的粗粒弹性网络模型，用于快速计算正常模式。使用闭合形式的逆运动学实现来自粗粒子的All-Atom模型的精确重建。结果显示了在单个处理器上计算时间几小时内模拟蛋白质化构象转变的方法。一组十种蛋白质的测试表明计算时间与蛋白质大小线性缩放，独立于蛋白质拓扑。腺苷酸激酶的进一步实验表明，在计算的路径中捕获该蛋白质的开放和闭合构象之间的过渡的主要特征。

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