Pandurangan Arun Prasad, Vasishtan Daven, Alber Frank, Topf Maya
Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK.
Division of Structural Biology, Oxford Particle Imaging Centre, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
Structure. 2015 Dec 1;23(12):2365-2376. doi: 10.1016/j.str.2015.10.013. Epub 2015 Nov 19.
We have developed a genetic algorithm for building macromolecular complexes using only a 3D-electron microscopy density map and the atomic structures of the relevant components. For efficient sampling the method uses map feature points calculated by vector quantization. The fitness function combines a mutual information score that quantifies the goodness of fit with a penalty score that helps to avoid clashes between components. Testing the method on ten assemblies (containing 3-8 protein components) and simulated density maps at 10, 15, and 20 Å resolution resulted in identification of the correct topology in 90%, 70%, and 60% of the cases, respectively. We further tested it on four assemblies with experimental maps at 7.2-23.5 Å resolution, showing the ability of the method to identify the correct topology in all cases. We have also demonstrated the importance of the map feature-point quality on assembly fitting in the lack of additional experimental information.
我们开发了一种遗传算法，仅使用三维电子显微镜密度图和相关组件的原子结构来构建大分子复合物。为了进行高效采样，该方法使用通过矢量量化计算得到的图谱特征点。适应度函数将量化拟合优度的互信息得分与有助于避免组件之间冲突的惩罚得分相结合。在十个组件（包含3 - 8个蛋白质组件）以及分辨率为10 Å、15 Å和20 Å的模拟密度图上对该方法进行测试，结果分别在90%、70%和60%的情况下识别出了正确的拓扑结构。我们还在四个具有分辨率为7.2 - 23.5 Å的实验图谱的组件上对其进行了测试，结果表明该方法在所有情况下都能够识别出正确的拓扑结构。我们还证明了在缺乏额外实验信息的情况下，图谱特征点质量对组件拟合的重要性。
