To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse‐grained elastic models followed by atomistic backbone refinement and side‐chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine‐learning protocols to optimize the combination of the sampling‐enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1‐2 hours for an average‐size protein) and simpler (using only pocket scores), so it is suitable for high‐throughput processing of large datasets of protein structures at the genome scale.

中文翻译：

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基于正常模式指导的构象采样预测隐性配体结合位点

为了大大扩展可药物基因组，对结合靶蛋白的小分子的隐蔽位点的快速准确预测是非常需要的。在这项研究中，我们开发了一种快速且简单的构象采样方案，该方案由正常模式引导，该模式由粗粒度弹性模型求解，然后进行原子骨架细化和侧链重新包装。尽管观察到与配体结合相关的复杂且多样的构象变化，但我们发现，沿着最低的30个模式中的每一个进行简单采样对于充分重构隐含位点而言是最佳的，因此可以通过现有的口袋发现程序（如fpocket和凹度）进行检测。我们进一步训练了机器学习协议，以优化采样增强型口袋得分与其他动态得分和保守得分的组合，这只会稍微改善性能。根据对84个已知隐蔽位点的训练集和对14种蛋白质的测试集的评估，我们的方法达到了与CryptoSite服务器相当的预测准确性（接收器工作特征曲线下的面积> 0.8）。与CryptoSite和其他基于广泛分子动力学模拟的方法相比，我们的方法速度更快（平均大小的蛋白质为1-2小时）并且更简单（仅使用口袋分数），因此适用于大批量的高通量处理基因组规模的蛋白质结构数据集。8）与CryptoSite服务器相当。与CryptoSite和其他基于广泛分子动力学模拟的方法相比，我们的方法速度更快（平均大小的蛋白质为1-2小时）并且更简单（仅使用口袋分数），因此适用于大批量的高通量处理基因组规模的蛋白质结构数据集。8）与CryptoSite服务器相当。与CryptoSite和其他基于广泛分子动力学模拟的方法相比，我们的方法速度更快（平均大小的蛋白质为1-2小时）并且更简单（仅使用口袋分数），因此适用于大批量的高通量处理基因组规模的蛋白质结构数据集。