Insilichem, Departament de Química, Universitat Autònoma de Barcelona, Edifici C.n., 08193 Cerdanyola del Vallés, Barcelona, Spain.
Biofisika Institute (UPV/EHU, CSIC) and Department of Biochemistry and Molecular Biology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Spain.
J Chem Inf Model. 2021 Jan 25;61(1):311-323. doi: 10.1021/acs.jcim.0c00827. Epub 2020 Dec 18.
With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.
大量的研究致力于解码金属物种与蛋白质的结合方式，因此计算机模拟方法是实验程序的有趣盟友。迄今为止，计算预测器主要通过识别目标蛋白与金属结合模板的最佳可能序列或结构匹配来工作。这些方法从根本上专注于金属的第一配体层。在这里，我们提出了 BioMetAll 预测器，它基于一个不同的假设：潜在金属结合位点的形成与蛋白质骨架的几何组织有关。我们首先报告算法所需的骨架便利几何描述符集及其从统计分析进行参数化。然后，介绍了 BioMetAll 在一组超过 90 个金属结合 X 射线结构上的成功基准测试。由于 BioMetAll 允许进行结构预测，而无需考虑侧链的精确几何形状，因此对于结构（无论是实验的还是理论的）不是金属结合位点最佳的系统，它显得非常有价值。我们在这里报告了它在三个不同的挑战性案例中的应用：（i）人血清白蛋白构象转变过程中金属结合位点的调节，（ii）血蓝蛋白中金属迁移途径的识别，以及（iii）产生方便的金属结合位点用于生物催化剂的突变预测。这项研究表明，BioMetAll 为无机化学和生物学界面的众多研究领域提供了一个通用的平台，并强调了蛋白质骨架预组织作为金属结合标记的作用。BioMetAll 是一个可在 https://github.com/insilichem/biometall 上获得的开源应用程序。
