论文标题
解决蛋白质对接骨架灵活性的进步
Advances to tackle backbone flexibility in protein docking
论文作者
论文摘要
计算对接方法可以提供蛋白质 - 蛋白质复合物的结构模型，但是关联时蛋白质骨架的灵活性通常会阻止准确的预测。在最近的盲目挑战中，在不到20％的“困难”目标（具有重大的骨干变化或不确定性）中，中等或高精度模型。在这里，我们描述了蛋白质 - 蛋白质对接的最新发展，并突出了应对骨干柔韧性的进步。在分子动力学和蒙特卡洛方法中，增强的采样技术的时间尺度限制减少了。内部坐标配方现在可以使用谐波动力学捕获单体和复合物的现实运动。机器学习方法适应地指导对接轨迹或从培训蛋白质界面训练的深神经网络中产生新颖的结合位点预测。这些工具可以使该领域保持良好的挑战，即正确预测具有重大构象变化的复杂结构。
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the "difficult" targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
