Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Program in Molecular Biophysics, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
Curr Opin Struct Biol. 2021 Apr;67:178-186. doi: 10.1016/j.sbi.2020.11.011. Epub 2020 Dec 23.
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.
计算对接方法可以提供蛋白质-蛋白质复合物的结构模型，但在结合时蛋白质骨架的灵活性常常阻碍了准确的预测。在最近的盲测挑战中，只有不到 20%的“困难”目标（具有显著的骨架变化或不确定性）提交了中等或高精度的模型。在这里，我们描述了蛋白质-蛋白质对接的最新进展，并强调了解决骨架灵活性问题的进展。在分子动力学和蒙特卡罗方法中，增强采样技术减少了时间尺度的限制。现在，内部坐标公式可以使用调和动力学来捕捉单体和复合物的真实运动。机器学习方法可以自适应地引导对接轨迹，或者从基于蛋白质界面训练的深度神经网络生成新的结合位点预测。这些工具使该领域有希望突破正确预测具有显著构象变化的复合物结构的长期挑战。
