Creating protein models from electron-density maps using particle-filtering methods
Creating protein models from electron-density maps using particle-filtering methods
复制标题
DOI：
10.1093/bioinformatics/btm480
复制
发表时间：
2007-11-01
期刊：
影响因子：
5.8
通讯作者：
Shavlik, Jude W.
中科院分区：
文献类型：
--
作者：
DiMaio, Frank;Kondrashov, Dmitry A.;Shavlik, Jude W.
动机：高通量蛋白质结晶学的一个瓶颈是解释电子密度图，也就是说，将分子模型与3D图像结晶学相匹配。之前，我们开发了ACMI(自动晶体图谱解释程序)，这是一种使用概率模型来推断准确的蛋白质骨架布局的算法。在这里，我们使用一种被称为粒子过滤的采样方法来产生一组全原子蛋白质模型。我们使用ACMI的输出来指导粒子过滤器的采样，产生一组准确的、物理上可行的结构集。结果：我们在10张质量较差的实验密度图上测试了我们的算法。我们发现，与简单地将最匹配的边链放置在ACMI的轨迹上相比，粒子滤波可以产生准确的全原子模型，从而减少链的数量，降低边链的均方根误差和减小R因子。结果表明，在主链完备性、侧链识别和结晶学R因子方面，我们的方法比三种主要的方法--TEXTAL、RESOLE和ARP/WARP--产生了更准确的模型。
Motivation: One bottleneck in high-throughput protein crystallography is interpreting an electron-density map, that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed Acmi (Automatic Crystallographic Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbone layout. Here, we use a sampling method known as particle filtering to produce a set of all-atom protein models. We use the output of Acmi to guide the particle filters sampling, producing an accurate, physically feasible set of structures.Results: We test our algorithm on 10 poor-quality experimental density maps. We show that particle filtering produces accurate all-atom models, resulting in fewer chains, lower sidechain RMS error and reduced R factor, compared to simply placing the best-matching sidechains on Acmi's trace. We show that our approach produces a more accurate model than three leading methods Textal, Resolve and ARP/wARP-in terms of main chain completeness, sidechain identification and crystallographic R factor.