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首页> 外文会议>Transactions on Computational Systems Biology VIII; Lecture Notes in Bioinformatics; 4780 >A Computationally Fast and Parametric Model to Estimate Protein-Ligand Docking Time for Stochastic Event Based Simulation
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A Computationally Fast and Parametric Model to Estimate Protein-Ligand Docking Time for Stochastic Event Based Simulation

机译：基于随机事件的估计蛋白质配体对接时间的计算快速参数模型

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This paper presents a computationally fast analytical model to estimate the time taken for protein-ligand docking in biological pathways. The environment inside the cell has been reported to be unstable with a considerable degree of randomness creating a stochastic resonance. To facilitate the understanding of the dynamic behavior of biological systems, we propose an "in silico" stochastic event based simulation. The implementation of this simulation requires the computation of the execution times of different biological events such as the protein-ligand docking process (time required for ligand-protein binding) as a random variable. The next event time of the system is computed by adding the event execution time to the clock value of the event start time. Our mathematical model takes special consideration of the actual biological process of ligand-protein docking with emphasis on the structural configurations of the ligands, proteins and the binding mechanism that enable us to control the model parameters considerably. We use a modification of the collision theory based approach to capture the randomness of this problem in discrete time and estimate the first two moments of this process. The numerical results for the first moment show promising correspondence with experimental results and demonstrate the efficacy of our model.
机译：本文提出了一种计算快速的分析模型，以估算蛋白质-配体对接在生物途径中所花费的时间。据报道，细胞内部的环境是不稳定的，具有相当大的随机性，从而产生了随机共振。为了促进对生物系统动态行为的理解，我们提出了一种基于“计算机模拟”的随机事件模拟。此模拟的实现需要计算不同生物事件的执行时间，例如蛋白质-配体对接过程（配体-蛋白质结合所需的时间）作为随机变量。通过将事件执行时间与事件开始时间的时钟值相加，可以计算出系统的下一个事件时间。我们的数学模型特别考虑了配体-蛋白质对接的实际生物学过程，并着重于配体，蛋白质的结构构型和结合机理，这使我们能够大量控制模型参数。我们使用基于碰撞理论的方法的改进，以捕获离散时间中该问题的随机性，并估计该过程的前两个时刻。最初的数值结果显示与实验结果有希望的对应，并证明了我们模型的有效性。

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