Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors
Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors
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DOI：
10.1021/acsnano.2c00301
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发表时间：
2022-04-26
期刊：
影响因子：
17.1
通讯作者：
Van Lehn, Reid C.
中科院分区：
文献类型：
--
作者：
Chew, Alex K.;Pedersen, Joel A.;Van Lehn, Reid C.
金纳米粒子是生物应用的通用材料，因为它们的性质可以通过在其表面组装配体以形成单分子膜来调节。然而，单分子层保护的纳米粒子在生物环境中的物理化学性质和行为是难以预测的，因为它们来自配体-配体和配体-溶剂相互作用的相互作用，而这些相互作用不能仅从配体化学结构容易地推断出来。在这项工作中，我们证明了ELS可以采用分子动力学模拟计算的描述符来预测纳米颗粒的性质和细胞摄取。我们进行了原子分子动力学模拟的154单层保护的金纳米粒子，并计算了一个小的库的模拟衍生的描述符，捕捉纳米粒子的结构和化学性质的水溶液。然后，我们使用可解释的回归算法来参数化QNAR模型，以预测从策展数据库获得的纳米颗粒辛醇-水分配系数，zeta电位和细胞摄取的实验测量值。这些模型表明，模拟衍生的描述符可以准确地预测实验趋势，并提供物理洞察什么描述符是最重要的，以获得所需的纳米粒子的性质或行为在生物环境中。最后，我们通过预测原始数据集中未包含的12种纳米颗粒的细胞摄取趋势来证明模型的可推广性。这些结果表明，QNAR模型参数化模拟衍生的描述符是准确的，可推广的计算工具，可用于指导设计单层保护的金纳米粒子的生物应用，而无需费力的试错实验。
Gold nanoparticles are versatile materials for biological applications because their properties can be modulated by assembling ligands on their surface to form monolayers. However, the physicochemical properties and behaviors of monolayer-protected nanoparticles in biological environments are difficult to anticipate because they emerge from the interplay of ligand-ligand and ligand-solvent interactions that cannot be readily inferred from ligand chemical structure alone. In this work, we demonstrate that els can employ descriptors calculated from molecular dynamics simulations to predict nanoparticle properties and cellular uptake. We performed atomistic molecular dynamics simulations of 154 monolayer-protected gold nanoparticles and calculated a small library of simulation-derived descriptors that capture nanoparticle structural and chemical properties in aqueous solution. We then parametrized QNAR models using interpretable regression algorithms to predict experimental measurements of nanoparticle octanol-water partition coefficients, zeta potentials, and cellular uptake obtained from a curated database. These models reveal that simulation-derived descriptors can accurately predict experimental trends and provide physical insight into what descriptors are most important for obtaining desired nanoparticle properties or behaviors in biological environments. Finally, we demonstrate model generalizability by predicting cell uptake trends for 12 nanoparticles not included in the original data set. These results demonstrate that QNAR models parametrized with simulation-derived descriptors are accurate, generalizable computational tools that could be used to guide the design of monolayer-protected gold nanoparticles for biological applications without laborious trial-and-error experimentation.