DataSheet_1_Integrative computational framework to decipher the functions of shell proteins in biomineralization.pdf

Mollusk shells contain biominerals with remarkable mechanical properties enabled by a small fraction of embedded organic matrix proteins. However, the specific molecular functions of most shell proteins have remained elusive. Traditional genomics and functional studies are extremely laborious to identify key components. To address this, we developed an in-silico pipeline integrating protein structure modeling, molecular dynamics simulations, and machine learning to elucidate the critical ion protein interactions governing shell formation. Using the pearl oyster Pinctada fucata as a test case, our framework successfully recapitulated known protein functions and predicted roles of uncharacterized proteins to guide future experiments. Moreover, the pipeline’s modular design enables versatile applications for rapidly elucidating structure-function relationships in diverse biomineralization systems, complementing conventional wet-lab methods. Overall, this computational approach leverages automatic simulations and analytics to unlock molecular insights into shell protein ion dynamics, accelerating the discovery of key crystallization regulators for bioinspired materials design.

软体动物贝壳中含有具备优异力学性能的生物矿物，其性能由少量嵌入的有机基质蛋白赋予。然而，绝大多数贝壳蛋白的具体分子功能仍未得到阐明。传统基因组学与功能研究在鉴定关键组分时往往极为费力。为此，我们开发了一套整合蛋白结构建模、分子动力学模拟与机器学习的计算流程（in-silico pipeline），以阐明调控贝壳形成的关键离子-蛋白相互作用。我们以马氏珠母贝（Pinctada fucata）作为测试对象，该框架成功复现了已知蛋白的功能，并对未表征蛋白的作用进行了预测，可为后续实验提供指导。此外，该流程采用模块化设计，可灵活应用于多种生物矿化系统中结构-功能关系的快速解析，作为传统湿实验方法的有力补充。总体而言，该计算方法借助自动化模拟与分析手段，揭示了贝壳蛋白离子动态互作的分子机制，加速了关键结晶调控因子的发现进程，可为仿生材料设计提供支撑。