摘要
背景:静电相互作用是引导分子与蛋白质结合的力量之一。通过计算方法评估这种相互作用使评估蛋白质-药物复合物的能量成为可能。 目的:我们在这里的目的是回顾一些用于计算蛋白质-药物复合物静电能的方法,并探索这些使用评分函数空间的抽象方法生成新的药物发现计算工具的能力。 方法:在这里,我们概述了AutoDock4半经验评分函数,用于计算蛋白-药物复合物的结合亲和力。我们的重点是静电相互作用,以及如何探索最近发表的结果,以提高计算模型的预测性能,以估计蛋白质-药物相互作用的能量。我们使用Binding MOAD、BindingDB和PDBbind提供的公共数据来评估不同方法预测绑定亲和性的预测性能。 结果:给出了一个用于评估对接项目中可用势能的评分函数的综合大纲。预测蛋白质-药物能量学计算模型的最新发展能够创建靶向评分函数来预测与这些蛋白质的结合。这些目标模型优于经典的评分函数,并强调了静电相互作用在绑定定义中的重要性。 结论:在这里,我们回顾了通过应用半经验自由能评分函数来预测结合亲和力的评分函数的发展。我们的研究表明,与经典评分函数相比,机器学习模型具有更好的预测性能,静电相互作用对结合亲和力的重要性。
关键词: 半经验力评分函数,介电常数函数参数,蛋白-配体相互作用,药物设计,静电相互作用,AutoDock4,评分函数空间。
Current Medicinal Chemistry
Title:Electrostatic Potential Energy in Protein-Drug Complexes
Volume: 28 Issue: 24
关键词: 半经验力评分函数,介电常数函数参数,蛋白-配体相互作用,药物设计,静电相互作用,AutoDock4,评分函数空间。
摘要:
Background: Electrostatic interactions are one of the forces guiding the binding of molecules to proteins. The assessment of this interaction through computational approaches makes it possible to evaluate the energy of protein-drug complexes.
Objective: Our purpose here is to review some of the methods used to calculate the electrostatic energy of protein-drug complexes and explore the capacity of these approaches for the generation of new computational tools for drug discovery using the abstraction of scoring function space.
Methods: Here, we present an overview of the AutoDock4 semi-empirical scoring function used to calculate binding affinity for protein-drug complexes. We focus our attention on electrostatic interactions and how to explore recently published results to increase the predictive performance of the computational models to estimate the energetics of protein- drug interactions. Public data available at Binding MOAD, BindingDB, and PDBbind were used to review the predictive performance of different approaches to predict binding affinity.
Results: A comprehensive outline of the scoring function used to evaluate potential energy available in docking programs is presented. Recent developments of computational models to predict protein-drug energetics were able to create targeted-scoring functions to predict binding to these proteins. These targeted models outperform classical scoring functions and highlight the importance of electrostatic interactions in the definition of the binding.
Conclusion: Here, we reviewed the development of scoring functions to predict binding affinity through the application of a semi-empirical free energy scoring function. Our studies show the superior predictive performance of machine learning models when compared with classical scoring functions and the importance of electrostatic interactions for binding affinity.
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Cite this article as:
Electrostatic Potential Energy in Protein-Drug Complexes, Current Medicinal Chemistry 2021; 28 (24) . https://dx.doi.org/10.2174/0929867328666210201150842
DOI https://dx.doi.org/10.2174/0929867328666210201150842 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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