Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Structure. 2019 Jun 4;27(6):1041-1051.e8. doi: 10.1016/j.str.2019.03.014. Epub 2019 Apr 18.
Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries-starting from homology-modeled monomers-we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes.
同型同源蛋白质在生命的各个领域无处不在,有关其结构的信息对于揭示其功能至关重要。这些复合物的大小通常使得它们难以通过高分辨率结构测定实验来解决。计算对接算法为模拟具有任意对称性的大型复合物提供了一种很有前途的替代方法。然而,现有算法的准确性受到使用同源建模单体时骨架不准确的限制。在这里,我们提出了 Rosetta SymDock2,它使用六维粗粒度评分函数广泛搜索对称构象空间,然后进行全原子柔性骨架精修,我们证明这对于紧密堆积复合物的物理现实建模至关重要。在不同点对称基准复合物集的全局对接中(从同源建模单体开始),我们成功对接(定义为在五个得分最高的模型中预测三个接近天然的结构)31 个环状复合物中的 17 个和 12 个二面角复合物中的 3 个。