Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/Darwin 3 and Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente, 28049, Cantoblanco (Madrid), Spain.
Bioinformatics. 2014 Oct 15;30(20):2891-8. doi: 10.1093/bioinformatics/btu404. Epub 2014 Jun 27.
Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. The reconstruction process leading to the final 3D map requires an approximate initial model. Generation of an initial model is still an open and challenging problem in single-particle analysis.
We present a fast and efficient approach to obtain a reliable, low-resolution estimation of the 3D structure of a macromolecule, without any a priori knowledge, addressing the well-known issue of initial volume estimation in the field of single-particle analysis. The input of the algorithm is a set of class average images obtained from individual projections of a biological object at random and unknown orientations by transmission electron microscopy micrographs. The proposed method is based on an initial non-lineal dimensionality reduction approach, which allows to automatically selecting representative small sets of class average images capturing the most of the structural information of the particle under study. These reduced sets are then used to generate volumes from random orientation assignments. The best volume is determined from these guesses using a random sample consensus (RANSAC) approach. We have tested our proposed algorithm, which we will term 3D-RANSAC, with simulated and experimental data, obtaining satisfactory results under the low signal-to-noise conditions typical of cryo-electron microscopy.
The algorithm is freely available as part of the Xmipp 3.1 package [http://xmipp.cnb.csic.es].
Supplementary data are available at Bioinformatics online.
大分子复合物的结构信息为它们执行生物功能的方式提供了关键的见解。导致最终 3D 图谱的重建过程需要一个近似的初始模型。初始模型的生成仍然是单颗粒分析中的一个开放和具有挑战性的问题。
我们提出了一种快速有效的方法，能够在没有任何先验知识的情况下，获得大分子 3D 结构的可靠、低分辨率估计，解决了单颗粒分析领域中众所周知的初始体积估计问题。该算法的输入是一组通过透射电子显微镜显微照片以随机和未知方向获得的生物物体的单个投影的类平均图像。所提出的方法基于初始非线性降维方法，该方法允许自动选择代表捕捉研究粒子的大部分结构信息的类平均图像的小集合。然后，从这些随机取向分配中使用这些减少的集合来生成体积。从这些猜测中使用随机样本一致性（RANSAC）方法确定最佳体积。我们已经使用模拟和实验数据测试了我们提出的算法，该算法我们将其称为 3D-RANSAC，在典型的低温电子显微镜低信噪比条件下获得了令人满意的结果。
该算法作为 Xmipp 3.1 包的一部分免费提供[http://xmipp.cnb.csic.es]。
补充数据可在“Bioinformatics”在线获取。
