Hefei National Laboratory for Physical Science at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
Sci Rep. 2016 Jul 5;6:29360. doi: 10.1038/srep29360.
Various low-resolution experimental techniques have gained more and more popularity in obtaining structural information of large biomolecules. In order to interpret the low-resolution structural data properly, one may need to construct an atomic model of the biomolecule by fitting the data using computer simulations. Here we develop, to our knowledge, a new computational tool for such integrative modeling by taking the advantage of an efficient sampling technique called parallel cascade selection (PaCS) simulation. For given low-resolution structural data, this PaCS-Fit method converts it into a scoring function. After an initial simulation starting from a known structure of the biomolecule, the scoring function is used to pick conformations for next cycle of multiple independent simulations. By this iterative screening-after-sampling strategy, the biomolecule may be driven towards a conformation that fits well with the low-resolution data. Our method has been validated using three proteins with small-angle X-ray scattering data and two proteins with electron microscopy data. In all benchmark tests, high-quality atomic models, with generally 1-3 Å from the target structures, are obtained. Since our tool does not need to add any biasing potential in the simulations to deform the structure, any type of low-resolution data can be implemented conveniently.
各种低分辨率实验技术在获取生物大分子的结构信息方面越来越受欢迎。为了正确解释低分辨率结构数据，人们可能需要通过计算机模拟拟合数据来构建生物分子的原子模型。在这里，我们利用一种称为并行级联选择（PaCS）模拟的高效采样技术，开发了一种用于这种综合建模的新计算工具。对于给定的低分辨率结构数据，该 PaCS-Fit 方法将其转换为评分函数。在从生物分子的已知结构开始的初始模拟之后，该评分函数用于为下一个独立模拟循环选择构象。通过这种迭代筛选-后采样策略，生物分子可以被驱动到与低分辨率数据拟合良好的构象。我们的方法已通过三个具有小角度 X 射线散射数据的蛋白质和两个具有电子显微镜数据的蛋白质进行了验证。在所有基准测试中，都获得了高质量的原子模型，通常与目标结构相差 1-3Å。由于我们的工具在模拟中不需要添加任何偏置势来改变结构，因此可以方便地实现任何类型的低分辨率数据。
