Centro Nac. Biotecnología (CSIC), c/Darwin, 3, 28049 Cantoblanco, Madrid, Spain; Univ. Autonoma de Madrid, 28049 Cantoblanco, Madrid, Spain.
Centro Nac. Biotecnología (CSIC), c/Darwin, 3, 28049 Cantoblanco, Madrid, Spain.
J Struct Biol. 2023 Dec;215(4):108024. doi: 10.1016/j.jsb.2023.108024. Epub 2023 Sep 11.
Single particle analysis (SPA) in cryo-electron microscopy (cryo-EM) is highly used to obtain the near-atomic structure of biological macromolecules. The current methods allow users to produce high-resolution maps from many samples. However, there are still challenging cases that require extra processing to obtain high resolution. This is the case when the macromolecule of the sample is composed of different components and we want to focus just on one of them. For example, if the macromolecule is composed of several flexible subunits and we are interested in a specific one, if it is embedded in a viral capsid environment, or if it has additional components to stabilize it, such as nanodiscs. The signal from these components, which in principle we are not interested in, can be removed from the particles using a projection subtraction method. Currently, there are two projection subtraction methods used in practice and both have some limitations. In fact, after evaluating their results, we consider that the problem is still open to new solutions, as they do not fully remove the signal of the components that are not of interest. Our aim is to develop a new and more precise projection subtraction method, improving the performance of state-of-the-art methods. We tested our algorithm with data from public databases and an in-house data set. In this work, we show that the performance of our algorithm improves the results obtained by others, including the localization of small ligands, such as drugs, whose binding location is unknown a priori.
在冷冻电子显微镜（cryo-EM）中，单颗粒分析（SPA）被广泛用于获得生物大分子的近原子结构。目前的方法允许用户从许多样本中生成高分辨率的图谱。然而，仍然存在一些具有挑战性的情况，需要额外的处理才能获得高分辨率。这种情况通常发生在样本中的大分子由不同的成分组成，而我们只想关注其中的一个成分。例如，如果大分子由几个灵活的亚基组成，而我们只对其中一个亚基感兴趣，如果它嵌入在病毒衣壳环境中，或者如果它有额外的成分来稳定它，例如纳米盘。这些成分的信号，原则上我们不感兴趣，可以使用投影扣除方法从粒子中去除。目前，有两种投影扣除方法在实践中使用，它们都有一些局限性。事实上，在评估它们的结果后，我们认为这个问题仍然需要新的解决方案，因为它们不能完全去除不感兴趣的成分的信号。我们的目标是开发一种新的、更精确的投影扣除方法，以提高最先进方法的性能。我们使用公共数据库和内部数据集的数据来测试我们的算法。在这项工作中，我们表明我们的算法的性能提高了其他算法的结果，包括小分子配体（如药物）的定位，这些配体的结合位置事先是未知的。
