Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations—e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application ( https://acc2.ncbr.muni.cz ) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets.

中文翻译：

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可通过 Web 应用程序访问的肽的优化 SQE 原子电荷

部分原子电荷在计算化学、化学信息学、生物信息学和纳米科学中有许多应用。目前，常用的电荷计算方法有电负性均衡法（EEM）、电荷平衡法（QEq）和扩展QEq（EQeq）。它们都很快，即使对于大分子也是如此，但需要经验参数。然而，即使是这些先进的方法也有局限性——例如，它们对肽、蛋白质和其他大分子的应用是有问题的。一种有望用于肽和其他大分子系统的经验电荷计算方法是分裂电荷平衡法 (SQE) 及其扩展 SQE+q0。不幸的是，这些方法只有一个参数集可用，而且它们的实现不容易访问。在本文中，我们首次提出了一种优化的引导最小化方法 (optGM)，用于经验电荷计算方法的快速参数化，并将其与当前可用的引导最小化 (GDMIN) 方法进行比较。然后，我们引入了 SQE、SQE+qp 的进一步扩展，适用于肽数据集，并将其与常用方法 EEM、QEq EQeq、SQE 和 SQE+q0 进行比较。最后，我们将 SQE 和 SQE+qp 集成到 Web 应用程序 Atomic Charge Calculator II (ACC II) 中，包括几个参数集。本文的主要贡献在于，它使用户可以通过 ACC II Web 应用程序 (https://acc2.ncbr.muni.cz) 和命令行应用程序访问 SQE 方法及其参数。此外，我们的改进 SQE+qp 为肽数据集提供了出色的解决方案。此外，optGM 在明显更短的时间内提供与 GDMIN 相当的参数。因此，optGM 允许我们使用大型数据集对具有更多参数（例如 SQE 及其扩展）的电荷计算方法进行参数化。