The Python-based program, XMECP, is developed for realizing robust, efficient, and state-of-the-art minimum energy crossing point (MECP) optimization in multiscale complex systems. This article introduces the basic capabilities of the XMECP program by theoretically investigating the MECP mechanism of several example systems including (1) the photosensitization mechanism of benzophenone, (2) photoinduced proton-coupled electron transfer in the cytosine–guanine base pair in DNA, (3) the spin-flip process in oxygen activation catalyzed by an iron-containing 2-oxoglutarate-dependent oxygenase (Fe/2OGX), and (4) the photochemical pathway of flavoprotein adjusted by the intensity of an external electric field. MECPs related to multistate reaction and multistate reactivity in large-scale complex biochemical systems can be well-treated by workflows suggested by the XMECP program. The branching plane updating the MECP optimization algorithm is strongly recommended as it provides derivative coupling vector (DCV) with explicit calculation and can equivalently evaluate contributions from non-QM residues to DCV, which can be nonadiabatic coupling or spin–orbit coupling in different cases. In the discussed QM/MM examples, we also found that the influence on the QM region by DCV can occur through noncovalent interactions and decay with distance. In the example of DNA base pairs, the nonadiabatic coupling occurs across the π–π stacking structure formed in the double-helix system. In contrast to general intuition, in the example of Fe/2OGX, the central ferrous and oxygen part contribute little to the spin–orbit coupling; however, a nearby arginine residue, which is treated by molecular mechanics in the QM/MM method, contributes significantly via two hydrogen bonds formed with α-ketoglutarate (α-KG). This indicates that the arginine residue plays a significant role in oxygen activation, driving the initial triplet state toward the productive quintet state, which is more than the previous knowledge that the arginine residue can bind α-KG at the reaction site by hydrogen bonds.

中文翻译：

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XMECP：在多尺度复杂系统中实现最先进的 MECP 优化

基于 Python 的程序 XMECP 是为了在多尺度复杂系统中实现稳健、高效和最先进的最小能量交叉点 (MECP) 优化而开发的。本文通过理论上研究几个示例系统的 MECP 机制，介绍了 XMECP 程序的基本功能，包括（1）二苯甲酮的光敏机制，（2）DNA 中胞嘧啶-鸟嘌呤碱基对中的光诱导质子耦合电子转移，（ 3）由含铁2-氧化戊二酸依赖性加氧酶（Fe/2OGX）催化的氧活化中的自旋翻转过程，以及（4）通过外部电场强度调节的黄素蛋白的光化学途径。与大规模复杂生化系统中的多态反应和多态反应性相关的 MECP 可以通过 XMECP 程序建议的工作流程得到很好的处理。强烈推荐更新MECP优化算法的分支平面，因为它提供了具有显式计算的导数耦合向量（DCV），并且可以等效地评估非QM残基对DCV的贡献，在不同情况下可以是非绝热耦合或自旋轨道耦合。在讨论的 QM/MM 示例中，我们还发现 DCV 对 QM 区域的影响可以通过非共价相互作用和随距离衰减而发生。在 DNA 碱基对的例子中，非绝热耦合发生在双螺旋系统中形成的 π-π 堆叠结构上。与一般直觉相反，在 Fe/2OGX 的例子中，中心的亚铁和氧部分对自旋轨道耦合贡献很小；然而，附近的精氨酸残基在 QM/MM 方法中经过分子力学处理，通过与 α-酮戊二酸 (α-KG) 形成的两个氢键发挥了显着的作用。这表明精氨酸残基在氧活化中发挥着重要作用，推动初始三重态向生产性五重态发展，这超出了之前关于精氨酸残基可以通过氢键在反应位点结合α-KG的认识。