Department of Computer Science, Cornell University, Ithaca, New York 14853, USA.
Proteins. 2010 Feb 1;78(2):400-19. doi: 10.1002/prot.22550.
Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution.
确定一大组模型中的正确结合模式是蛋白质-蛋白质对接中的重要步骤。我们基于重叠面积确定了蛋白质对接筛选器，这显著减少了需要详细检查的候选结构的数量。我们还使用包含 640 个双链蛋白质复合物的综合学习集，通过数学规划，基于残基接触和重叠面积开发了势能。与其他公开可用的蛋白质对接势能相比，我们的势能在区分大型独立测试集（84 个复合物）上的天然和非天然结合模式方面具有更好的识别能力，而不依赖于我们的训练集。我们能够在 43 种情况下将接近天然的模型排在首位，在 51 种情况下排在前 10 位。我们还报告了一个原子势能，在 46 种情况下将接近天然的模型排在首位，在 58 种情况下排在前 10 位。我们的筛选器+势能非常适合选择一小部分要细化到原子分辨率的模型。
