Optimization of 3D Poisson–Nernst‐Planck model for fast evaluation of diverse protein channels. Issue 10 (19th August 2013)
- Record Type:
- Journal Article
- Title:
- Optimization of 3D Poisson–Nernst‐Planck model for fast evaluation of diverse protein channels. Issue 10 (19th August 2013)
- Main Title:
- Optimization of 3D Poisson–Nernst‐Planck model for fast evaluation of diverse protein channels
- Authors:
- Dyrka, Witold
Bartuzel, Maciej M.
Kotulska, Malgorzata - Abstract:
- <abstract abstract-type="main"> <title>ABSTRACT</title> <p>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<abstract abstract-type="main"> <title>ABSTRACT</title> <p>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. Proteins 2013; 81:1802–1822. © 2013 Wiley Periodicals, Inc.</p> </abstract> … (more)
- Is Part Of:
- Proteins. Volume 81:Issue 10(2013)
- Journal:
- Proteins
- Issue:
- Volume 81:Issue 10(2013)
- Issue Display:
- Volume 81, Issue 10 (2013)
- Year:
- 2013
- Volume:
- 81
- Issue:
- 10
- Issue Sort Value:
- 2013-0081-0010-0000
- Page Start:
- 1802
- Page End:
- 1822
- Publication Date:
- 2013-08-19
- Subjects:
- Proteins -- Periodicals
Proteins -- Periodicals
572.6 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/prot.24326 ↗
- Languages:
- English
- ISSNs:
- 0887-3585
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6936.164000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4111.xml