BACKGROUND Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences. RESULTS Our simulation experiments demonstrate that the method computes high-scoring conformations of large loops (>10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints. CONCLUSION Numerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.

中文翻译：

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根据概率先验对蛋白质环构象进行无偏、可扩展的采样。

背景技术蛋白质环是与功能密切相关的灵活结构，但理解环运动和生成环构象整体仍然是重大的计算挑战。离散搜索技术很难扩展到大循环，优化和分子动力学技术容易出现局部极小值，而逆运动学技术只能以临时方式结合结构偏好。本文提出了子环逆运动学蒙特卡罗 (SLIKMC)，这是一种新的马尔可夫链蒙特卡罗算法，用于根据实验可用的异构结构偏好生成闭环构象。结果我们的模拟实验表明，该方法计算大环（> 10 个残基）的高分构象的速度比标准蒙特卡罗和离散搜索技术快几个数量级。两项新进展有助于新方法的可扩展性。首先，通过概率图形模型（PGM）指定结构偏好，该模型在统一框架中链接构象变量、空间变量（例如原子位置）、约束和先验信息。该方法使用稀疏 PGM，利用原子和残基之间相互作用的局部性。其次，开发了一种对子循环进行采样的新方法，以生成受闭环约束限制的概率密度的统计无偏样本。结论 数值实验证实，SLIKMC 生成的构象集合在统计上与指定的结构偏好一致。在标准 PC 硬件上几秒钟内即可对具有 100 多个残基的蛋白质构象进行采样。对涉及离子结合的蛋白质的应用证明了其作为环系综生成和缺失结构补全工具的潜力。