<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>Quantifying Protein-Protein Interactions in Molecular Simulations
pmid: 32379446
pmc: PMC7294537
Quantifying Protein-Protein Interactions in Molecular Simulations
We present simple, accurate, and efficient methods to estimate the dissociation constant Kd and the second osmotic virial coefficient B2 from molecular simulations. We show that for simulations of two proteins in a box, Kd is determined by B2 and the fraction of bound protein. We present two different methods to calculate B2 from Monte Carlo and molecular dynamics simulations using implicit or explicit solvent. We derive a surprisingly simple expression for B2, adding significantly to the understanding of this important quantity. Non-binding interactions of proteins and other macromolecules shape the physicochemical properties of the crowded environments inside cells and of biomolecular condensates. We show how to extract the contributions of non-binding conformations to B2 and discuss how these can be determined in analytical ultracentrifugation and SAXS experiments. We expect that our methods will prove to be instrumental in force parameterization efforts and high-throughput studies of large interactomes.
- Max Planck Institute of Neurobiology Germany
- Goethe University Frankfurt Germany
- Max Planck Society Germany
Solvents, Proteins, Thermodynamics, Molecular Dynamics Simulation, Monte Carlo Method, Biophysical Phenomena
Solvents, Proteins, Thermodynamics, Molecular Dynamics Simulation, Monte Carlo Method, Biophysical Phenomena
4 Research products, page 1 of 1
- 2003IsRelatedTo
- 1975IsRelatedTo
- IsRelatedTo
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).36 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
