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De novo determination of near-surface electrostatic potentials by NMR

Electrostatic potentials computed from three-dimensional structures of biomolecules by solving the Poisson–Boltzmann equation are widely used in molecular biophysics, structural biology, and medicinal chemistry. Despite the approximate nature of the Poisson–Boltzmann theory, validation of the comput...

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Autores principales: Yu, Binhan, Pletka, Channing C., Pettitt, B. Montgomery, Iwahara, Junji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237567/
https://www.ncbi.nlm.nih.gov/pubmed/34161285
http://dx.doi.org/10.1073/pnas.2104020118
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author Yu, Binhan
Pletka, Channing C.
Pettitt, B. Montgomery
Iwahara, Junji
author_facet Yu, Binhan
Pletka, Channing C.
Pettitt, B. Montgomery
Iwahara, Junji
author_sort Yu, Binhan
collection PubMed
description Electrostatic potentials computed from three-dimensional structures of biomolecules by solving the Poisson–Boltzmann equation are widely used in molecular biophysics, structural biology, and medicinal chemistry. Despite the approximate nature of the Poisson–Boltzmann theory, validation of the computed electrostatic potentials around biological macromolecules is rare and methodologically limited. Here, we present a unique and powerful NMR method that allows for straightforward and extensive comparison with electrostatic models for biomolecules and their complexes. This method utilizes paramagnetic relaxation enhancement arising from analogous cationic and anionic cosolutes whose spatial distributions around biological macromolecules reflect electrostatic potentials. We demonstrate that this NMR method enables de novo determination of near-surface electrostatic potentials for individual protein residues without using any structural information. We applied the method to ubiquitin and the Antp homeodomain–DNA complex. The experimental data agreed well with predictions from the Poisson–Boltzmann theory. Thus, our experimental results clearly support the validity of the theory for these systems. However, our experimental study also illuminates certain weaknesses of the Poisson–Boltzmann theory. For example, we found that the theory predicts stronger dependence of near-surface electrostatic potentials on ionic strength than observed in the experiments. Our data also suggest that conformational flexibility or structural uncertainties may cause large errors in theoretical predictions of electrostatic potentials, particularly for highly charged systems. This NMR-based method permits extensive assessment of near-surface electrostatic potentials for various regions around biological macromolecules and thereby may facilitate improvement of the computational approaches for electrostatic potentials.
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spelling pubmed-82375672021-07-03 De novo determination of near-surface electrostatic potentials by NMR Yu, Binhan Pletka, Channing C. Pettitt, B. Montgomery Iwahara, Junji Proc Natl Acad Sci U S A Physical Sciences Electrostatic potentials computed from three-dimensional structures of biomolecules by solving the Poisson–Boltzmann equation are widely used in molecular biophysics, structural biology, and medicinal chemistry. Despite the approximate nature of the Poisson–Boltzmann theory, validation of the computed electrostatic potentials around biological macromolecules is rare and methodologically limited. Here, we present a unique and powerful NMR method that allows for straightforward and extensive comparison with electrostatic models for biomolecules and their complexes. This method utilizes paramagnetic relaxation enhancement arising from analogous cationic and anionic cosolutes whose spatial distributions around biological macromolecules reflect electrostatic potentials. We demonstrate that this NMR method enables de novo determination of near-surface electrostatic potentials for individual protein residues without using any structural information. We applied the method to ubiquitin and the Antp homeodomain–DNA complex. The experimental data agreed well with predictions from the Poisson–Boltzmann theory. Thus, our experimental results clearly support the validity of the theory for these systems. However, our experimental study also illuminates certain weaknesses of the Poisson–Boltzmann theory. For example, we found that the theory predicts stronger dependence of near-surface electrostatic potentials on ionic strength than observed in the experiments. Our data also suggest that conformational flexibility or structural uncertainties may cause large errors in theoretical predictions of electrostatic potentials, particularly for highly charged systems. This NMR-based method permits extensive assessment of near-surface electrostatic potentials for various regions around biological macromolecules and thereby may facilitate improvement of the computational approaches for electrostatic potentials. National Academy of Sciences 2021-06-22 2021-06-14 /pmc/articles/PMC8237567/ /pubmed/34161285 http://dx.doi.org/10.1073/pnas.2104020118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Yu, Binhan
Pletka, Channing C.
Pettitt, B. Montgomery
Iwahara, Junji
De novo determination of near-surface electrostatic potentials by NMR
title De novo determination of near-surface electrostatic potentials by NMR
title_full De novo determination of near-surface electrostatic potentials by NMR
title_fullStr De novo determination of near-surface electrostatic potentials by NMR
title_full_unstemmed De novo determination of near-surface electrostatic potentials by NMR
title_short De novo determination of near-surface electrostatic potentials by NMR
title_sort de novo determination of near-surface electrostatic potentials by nmr
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237567/
https://www.ncbi.nlm.nih.gov/pubmed/34161285
http://dx.doi.org/10.1073/pnas.2104020118
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