Sorzano C O S, Vargas J, de la Rosa-Trevín J M, Otón J, Álvarez-Cabrera A L, Abrishami V, Sesmero E, Marabini R, Carazo J M
National Center of Biotechnology (CSIC), c/Darwin, 3, Campus Univ. Autonoma de Madrid, 28049 Cantoblanco, Madrid, Spain; Bioengineering Lab., Univ. San Pablo CEU, Campus Urb. Monteprincipe s/n, 28668 Boadilla del Monte, Madrid, Spain.
National Center of Biotechnology (CSIC), c/Darwin, 3, Campus Univ. Autonoma de Madrid, 28049 Cantoblanco, Madrid, Spain.
J Struct Biol. 2015 Mar;189(3):213-9. doi: 10.1016/j.jsb.2015.01.009. Epub 2015 Jan 28.
Cryo Electron Microscopy is a powerful Structural Biology technique, allowing the elucidation of the three-dimensional structure of biological macromolecules. In particular, the structural study of purified macromolecules -often referred as Single Particle Analysis(SPA)- is normally performed through an iterative process that needs a first estimation of the three-dimensional structure that is progressively refined using experimental data. It is well-known the local optimisation nature of this refinement, so that the initial choice of this first structure may substantially change the final result. Computational algorithms aiming to providing this first structure already exist. However, the question is far from settled and more robust algorithms are still needed so that the refinement process can be performed with sufficient guarantees. In this article we present a new algorithm that addresses the initial volume problem in SPA by setting it in a Weighted Least Squares framework and calculating the weights through a statistical approach based on the cumulative density function of different image similarity measures. We show that the new algorithm is significantly more robust than other state-of-the-art algorithms currently in use in the field. The algorithm is available as part of the software suite Xmipp (http://xmipp.cnb.csic.es) and Scipion (http://scipion.cnb.csic.es) under the name "Significant".
冷冻电子显微镜是一种强大的结构生物学技术，可用于阐明生物大分子的三维结构。特别是，对纯化的大分子进行结构研究（通常称为单颗粒分析，SPA），通常通过一个迭代过程来完成，该过程需要对三维结构进行初步估计，并利用实验数据逐步优化。众所周知，这种优化具有局部优化的性质，因此，第一个结构的初始选择可能会显著改变最终结果。旨在提供这种初始结构的计算算法已经存在。然而，这个问题远未解决，仍然需要更强大的算法，以便能够在有足够保障的情况下进行优化过程。在本文中，我们提出了一种新算法，该算法通过将其置于加权最小二乘框架中，并基于不同图像相似性度量的累积密度函数，通过统计方法计算权重，来解决单颗粒分析中的初始体积问题。我们表明，新算法比该领域目前使用的其他先进算法具有更强的鲁棒性。该算法作为软件套件Xmipp（http://xmipp.cnb.csic.es）和Scipion（http://scipion.cnb.csic.es）的一部分，以“Significant”的名称提供。
