Group of Bioinformatics and Biophysics of Nanopores, Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Wroclaw, Poland.
Proteins. 2013 Oct;81(10):1802-22. doi: 10.1002/prot.24326. Epub 2013 Aug 19.
We show the accuracy and applicability of our fast algorithmic implementation of a three-dimensional Poisson-Nernst-Planck (3D-PNP) flow model for characterizing different protein channels. Due to its high computational efficiency, our model can predict the full current-voltage characteristics of a channel within minutes, based on the experimental 3D structure of the channel or its computational model structure. Compared with other methods, such as Brownian dynamics, which currently needs a few weeks of the computational time, or even much more demanding molecular dynamics modeling, 3D-PNP is the only available method for a function-based evaluation of very numerous tentative structural channel models. Flow model tests of our algorithm and its optimal parametrization are provided for five native channels whose experimental structures are available in the protein data bank (PDB) in an open conductive state, and whose experimental current-voltage characteristics have been published. The channels represent very different geometric and structural properties, which makes it the widest test to date of the accuracy of 3D-PNP on real channels. We test whether the channel conductance, rectification, and charge selectivity obtained from the flow model, could be sufficiently sensitive to single-point mutations, related to unsignificant changes in the channel structure. Our results show that the classical 3D-PNP model, under proper parametrization, is able to achieve a qualitative agreement with experimental data for a majority of the tested characteristics and channels, including channels with narrow and irregular conductivity pores. We propose that although the standard PNP model cannot provide insight into complex physical phenomena due to its intrinsic limitations, its semiquantitative agreement is achievable for rectification and selectivity at a level sufficient for the bioinformatical purpose of selecting the best structural models with a great advantage of a very short computational time.
我们展示了我们快速算法实现三维泊松-纳斯特-普朗克（3D-PNP）流动模型的准确性和适用性，该模型用于描述不同的蛋白质通道。由于其计算效率高，我们的模型可以在几分钟内根据通道的实验 3D 结构或其计算模型结构预测通道的全电流-电压特性。与其他方法相比，例如布朗动力学，目前需要数周的计算时间，甚至更具挑战性的分子动力学建模，3D-PNP 是唯一可用于基于功能评估大量候选结构通道模型的方法。我们提供了算法及其最佳参数化的流动模型测试，用于五个具有开放导电状态的实验结构可在蛋白质数据库（PDB）中获得的天然通道，并且已经公布了其实验电流-电压特性。这些通道代表了非常不同的几何和结构特性，这使得它成为迄今为止对真实通道的 3D-PNP 准确性的最广泛测试。我们测试了从流动模型获得的通道电导、整流和电荷选择性是否能够对单点突变足够敏感，这些突变与通道结构的微小变化有关。我们的结果表明，在适当的参数化下，经典的 3D-PNP 模型能够在大多数测试特性和通道上实现与实验数据的定性一致，包括具有狭窄和不规则导电性孔的通道。我们提出，尽管由于其内在限制，标准 PNP 模型无法提供对复杂物理现象的深入了解，但由于其内在限制，它可以在整流和选择性方面达到半定量的一致，足以满足生物信息学目的，选择具有巨大优势的最佳结构模型，计算时间非常短。
