Departamento de Química, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, João Pessoa, Brazil.
J Chem Phys. 2023 May 28;158(20). doi: 10.1063/5.0132687.
In this Review, we reviewed the efforts to expand the applications of conceptual density functional theory reactivity descriptors and hard and soft acid and base principles for macromolecules and other strategies that focused on low-level quantum chemistry methods. Currently, recent applications are taking advantage of modifications of these descriptors using semiempirical electronic structures to explain enzymatic catalysis reactions, protein-binding processes, and structural analysis in proteins. We have explored these new solutions along with their implementations in the software PRIMoRDiA, discussing their impact on the field and its perspectives. We show the main issues in the analysis of the electronic structure of macromolecules, which are the application of the same calculation protocols used for small molecules without considering particularities in those large systems' electronic configuration. The major result of our discussions is that the use of semiempirical methods is crucial to obtain such a type of analysis, which can provide a powerful dimension of information and be part of future low-cost predictive tools. We expect semiempirical methods continue playing an important role in the quantum chemistry evaluation of large molecules. As computational resources advance, semiempirical methods might lead us to explore the electronic structure of even larger biological macromolecular entities and sets of structures representing larger timescales.
在这篇综述中，我们回顾了扩展概念密度泛函理论反应性描述符和软硬酸碱原理在大分子中的应用的努力，以及其他专注于低级量子化学方法的策略。目前，最近的应用利用这些描述符的半经验电子结构的修改来解释酶催化反应、蛋白质结合过程和蛋白质中的结构分析。我们探索了这些新的解决方案及其在 PRIMoRDiA 软件中的实现，讨论了它们对该领域的影响及其前景。我们展示了分析大分子电子结构的主要问题，这些问题是在不考虑这些大系统电子构型特殊性的情况下，应用于小分子的相同计算方案。我们讨论的主要结果是，使用半经验方法对于获得这种类型的分析至关重要，这种分析可以提供强大的信息维度，并成为未来低成本预测工具的一部分。我们预计半经验方法将继续在大分子的量子化学评估中发挥重要作用。随着计算资源的进步，半经验方法可能会引导我们探索更大的生物大分子实体和代表更大时间尺度的结构的电子结构。
