A long-standing goal in the field of biomineralization has been to achieve a molecular-level mechanistic understanding of how proteins participate in the nucleation and growth of inorganic crystals (both in vitro and in vivo). Computational methods offer an approach to explore these interactions and propose mechanisms at the atomic scale; however, to have confidence in the predictions of a computational method, the method must first be validated against a benchmark experimental data set of protein–mineral interactions. Relatively little work has been done to test the ability of computation to reproduce experimental results on mineral systems with biologically relevant additives present. The goal of this work is to develop a standard and varied benchmark to test whether a computational method is able to match experimental results at the length and time scales of biomineral–peptide interactions. We compare the results of the RosettaSurface algorithm to an experimental benchmark of kinetic and thermodynamic measurements on peptide–biomineral interactions taken from atomic force microscopy. The RosettaSurface algorithm successfully identifies which mineral face and step edges will bind peptides the strongest; however, the algorithm struggles to predict the correct rank order of binding for multiple peptides to the same face or step edge.

中文翻译：

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肽与生物矿物质相互作用的基准研究

生物矿化领域的长期目标是对蛋白质如何参与无机晶体的成核和生长（体外和体内）达到分子水平的机械理解。）。计算方法提供了一种探索这些相互作用并提出原子尺度机理的方法。但是，要对计算方法的预测有信心，必须首先根据蛋白质-矿物质相互作用的基准实验数据集对方法进行验证。相对而言，很少有工作来测试在具有生物学相关添加剂的矿物系统上重现实验结果的计算能力。这项工作的目的是开发一个标准的和多样化的基准，以测试一种计算方法是否能够在生物矿物质-肽相互作用的长度和时间尺度上与实验结果相匹配。我们将RosettaSurface算法的结果与从原子力显微镜获取的肽-生物矿物相互作用的动力学和热力学测量的实验基准进行了比较。RosettaSurface算法成功地识别出哪些矿物表面和台阶边缘将结合最强的肽。然而，该算法难以预测多个肽结合到同一面或台阶边缘的正确排列顺序。