Literature DB >> 34241219

Macromolecular crowding effects on electrostatic binding affinity: Fundamental insights from theoretical, idealized models.

Rachel Kim1, Mala L Radhakrishnan1.   

Abstract

The crowded cellular environment can affect biomolecular binding energetics, with specific effects depending on the properties of the binding partners and the local environment. Often, crowding effects on binding are studied on particular complexes, which provide system-specific insights but may not provide comprehensive trends or a generalized framework to better understand how crowding affects energetics involved in molecular recognition. Here, we use theoretical, idealized molecules whose physical properties can be systematically varied along with samplings of crowder placements to understand how electrostatic binding energetics are altered through crowding and how these effects depend on the charge distribution, shape, and size of the binding partners or crowders. We focus on electrostatic binding energetics using a continuum electrostatic framework to understand effects due to depletion of a polar, aqueous solvent in a crowded environment. We find that crowding effects can depend predictably on a system's charge distribution, with coupling between the crowder size and the geometry of the partners' binding interface in determining crowder effects. We also explore the effect of crowder charge on binding interactions as a function of the monopoles of the system components. Finally, we find that modeling crowding via a lowered solvent dielectric constant cannot account for certain electrostatic crowding effects due to the finite size, shape, or placement of system components. This study, which comprehensively examines solvent depletion effects due to crowding, complements work focusing on other crowding aspects to help build a holistic understanding of environmental impacts on molecular recognition.

Year:  2021        PMID: 34241219     DOI: 10.1063/5.0042082

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  1 in total

1.  How to Model for a Living: The CSGF as a Catalyst for Supermodels.

Authors:  M L Radhakrishnan
Journal:  Comput Sci Eng       Date:  2021-10-14       Impact factor: 2.152

  1 in total

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