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Quantifying Protein–Protein Interactions in Molecular Simulations

[Image: see text] Interactions among proteins, nucleic acids, and other macromolecules are essential for their biological functions and shape the physicochemcial properties of the crowded environments inside living cells. Binding interactions are commonly quantified by dissociation constants K(d), a...

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Autores principales: Jost Lopez, Alfredo, Quoika, Patrick K., Linke, Max, Hummer, Gerhard, Köfinger, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294537/
https://www.ncbi.nlm.nih.gov/pubmed/32379446
http://dx.doi.org/10.1021/acs.jpcb.9b11802
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author Jost Lopez, Alfredo
Quoika, Patrick K.
Linke, Max
Hummer, Gerhard
Köfinger, Jürgen
author_facet Jost Lopez, Alfredo
Quoika, Patrick K.
Linke, Max
Hummer, Gerhard
Köfinger, Jürgen
author_sort Jost Lopez, Alfredo
collection PubMed
description [Image: see text] Interactions among proteins, nucleic acids, and other macromolecules are essential for their biological functions and shape the physicochemcial properties of the crowded environments inside living cells. Binding interactions are commonly quantified by dissociation constants K(d), and both binding and nonbinding interactions are quantified by second osmotic virial coefficients B(2). As a measure of nonspecific binding and stickiness, B(2) is receiving renewed attention in the context of so-called liquid–liquid phase separation in protein and nucleic acid solutions. We show that K(d) is fully determined by B(2) and the fraction of the dimer observed in molecular simulations of two proteins in a box. We derive two methods to calculate B(2). From molecular dynamics or Monte Carlo simulations using implicit solvents, we can determine B(2) from insertion and removal energies by applying Bennett’s acceptance ratio (BAR) method or the (binless) weighted histogram analysis method (WHAM). From simulations using implicit or explicit solvents, one can estimate B(2) from the probability that the two molecules are within a volume large enough to cover their range of interactions. We validate these methods for coarse-grained Monte Carlo simulations of three weakly binding proteins. Our estimates for K(d) and B(2) allow us to separate out the contributions of nonbinding interactions to B(2). Comparison of calculated and measured values of K(d) and B(2) can be used to (re-)parameterize and improve molecular force fields by calibrating specific affinities, overall stickiness, and nonbinding interactions. The accuracy and efficiency of K(d) and B(2) calculations make them well suited for high-throughput studies of large interactomes.
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spelling pubmed-72945372020-06-16 Quantifying Protein–Protein Interactions in Molecular Simulations Jost Lopez, Alfredo Quoika, Patrick K. Linke, Max Hummer, Gerhard Köfinger, Jürgen J Phys Chem B [Image: see text] Interactions among proteins, nucleic acids, and other macromolecules are essential for their biological functions and shape the physicochemcial properties of the crowded environments inside living cells. Binding interactions are commonly quantified by dissociation constants K(d), and both binding and nonbinding interactions are quantified by second osmotic virial coefficients B(2). As a measure of nonspecific binding and stickiness, B(2) is receiving renewed attention in the context of so-called liquid–liquid phase separation in protein and nucleic acid solutions. We show that K(d) is fully determined by B(2) and the fraction of the dimer observed in molecular simulations of two proteins in a box. We derive two methods to calculate B(2). From molecular dynamics or Monte Carlo simulations using implicit solvents, we can determine B(2) from insertion and removal energies by applying Bennett’s acceptance ratio (BAR) method or the (binless) weighted histogram analysis method (WHAM). From simulations using implicit or explicit solvents, one can estimate B(2) from the probability that the two molecules are within a volume large enough to cover their range of interactions. We validate these methods for coarse-grained Monte Carlo simulations of three weakly binding proteins. Our estimates for K(d) and B(2) allow us to separate out the contributions of nonbinding interactions to B(2). Comparison of calculated and measured values of K(d) and B(2) can be used to (re-)parameterize and improve molecular force fields by calibrating specific affinities, overall stickiness, and nonbinding interactions. The accuracy and efficiency of K(d) and B(2) calculations make them well suited for high-throughput studies of large interactomes. American Chemical Society 2020-05-07 2020-06-11 /pmc/articles/PMC7294537/ /pubmed/32379446 http://dx.doi.org/10.1021/acs.jpcb.9b11802 Text en Copyright © 2020 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
spellingShingle Jost Lopez, Alfredo
Quoika, Patrick K.
Linke, Max
Hummer, Gerhard
Köfinger, Jürgen
Quantifying Protein–Protein Interactions in Molecular Simulations
title Quantifying Protein–Protein Interactions in Molecular Simulations
title_full Quantifying Protein–Protein Interactions in Molecular Simulations
title_fullStr Quantifying Protein–Protein Interactions in Molecular Simulations
title_full_unstemmed Quantifying Protein–Protein Interactions in Molecular Simulations
title_short Quantifying Protein–Protein Interactions in Molecular Simulations
title_sort quantifying protein–protein interactions in molecular simulations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294537/
https://www.ncbi.nlm.nih.gov/pubmed/32379446
http://dx.doi.org/10.1021/acs.jpcb.9b11802
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