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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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Detalles Bibliográficos
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
Descripción
Sumario:[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.