Abrahamsson Dimitri, Koronaiou Lelouda-Athanasia, Johnson Trevor, Yang Junjie, Ji Xiaowen, Lambropoulou Dimitra A
Department of Pediatrics, New York University Grossman School of Medicine New York 10016 USA
Department of Obstetrics, Gynecology and Reproductive Sciences, School of Medicine, University of California San Francisco California 94158 USA.
RSC Adv. 2024 Nov 22;14(50):37470-37482. doi: 10.1039/d4ra06695b. eCollection 2024 Nov 19.
Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high-resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the relative response factor of a compound (RRF) expressed as the peak area of a given peak divided by its concentration. In this paper, we explore the application of molecular dynamics (MD) in the development of a computational workflow for predicting RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations. We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then sampled to construct a multilinear regression model for predicting RRF using Monte Carlo simulations. The best performing model showed a coefficient of determination ( ) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed promise in predictions of RRF.
液相色谱（LC）电喷雾电离（ESI）高分辨率质谱（HRMS）技术的进步使其在环境和生物样品的非靶向分析（NTA）中成为越来越受欢迎的分析技术。NTA当前方法的一个关键限制是在生物和环境样品中检测到的许多化合物缺乏可用的分析标准品。计算方法可以通过对化合物的相对响应因子（RRF）进行建模来估计浓度，RRF表示为给定峰的峰面积除以其浓度。在本文中，我们探索了分子动力学（MD）在开发预测RRF的计算工作流程中的应用。我们使用液相色谱 - 四极杆飞行时间（QTOF）质谱仪获得了48种化合物的RRF测量值，并计算了它们的RRF。我们使用CGenFF力场生成拓扑结构，并使用GROMACS进行分子动力学（MD）模拟。我们计算了电喷雾液滴中分析物与所有其他分子之间的 Lennard-Jones 和库仑相互作用，然后对其进行采样以构建用于使用蒙特卡罗模拟预测RRF的多元线性回归模型。性能最佳的模型的决定系数（ ）为0.82，平均绝对误差（MAE）为0.13对数单位。这种性能与包括机器学习模型在内的其他预测模型相当。虽然需要对不同的化学结构进行进一步评估，但我们的方法在RRF预测方面显示出了前景。
